From 05d8a8acf2ce11305d1e919616c7284c507b03b1 Mon Sep 17 00:00:00 2001 From: BryanRumsey <44621966+BryanRumsey@users.noreply.github.com> Date: Wed, 17 Aug 2022 09:15:04 -0400 Subject: [PATCH 1/9] Updated version to v2.4.10 --- __version__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/__version__.py b/__version__.py index ff0dd475bb..24f6d5b956 100644 --- a/__version__.py +++ b/__version__.py @@ -5,7 +5,7 @@ # @website https://github.com/stochss/stochss # ============================================================================= -__version__ = '2.4.9' +__version__ = '2.4.10' __title__ = 'StochSS' __description__ = 'StochSS is an integrated development environment (IDE) \ for simulation of biochemical networks.' From 91e66b5b92006e1ab876e7f69d55f87cc5243613 Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Thu, 18 Aug 2022 11:21:18 -0400 Subject: [PATCH 2/9] Fixed issue with SBML conversion. --- stochss/handlers/util/stochss_model.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/stochss/handlers/util/stochss_model.py b/stochss/handlers/util/stochss_model.py index f5b0b22d4d..4a96cc541c 100644 --- a/stochss/handlers/util/stochss_model.py +++ b/stochss/handlers/util/stochss_model.py @@ -371,10 +371,10 @@ def convert_to_sbml(self): s_path = os.path.join(dirname, s_file) g_model = self.convert_to_gillespy2() - with export(g_model, path=tempfile.NamedTemporaryFile().name) as tmp_path: - self.log("debug", f"Temp path to the sbml file: {tmp_path}") - with open(tmp_path, "r", encoding="utf-8") as sbml_file: - s_doc = sbml_file.read() + tmp_path = export(g_model, path=tempfile.NamedTemporaryFile().name) + self.log("debug", f"Temp path to the sbml file: {tmp_path}") + with open(tmp_path, "r", encoding="utf-8") as sbml_file: + s_doc = sbml_file.read() message = f"{self.get_file()} was successfully converted to {s_file}" return {"Message":message}, {"path":s_path, "document":s_doc} From 308d6d3602afab20197c7ce4b8ce1cb84217e57b Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Thu, 18 Aug 2022 11:22:16 -0400 Subject: [PATCH 3/9] Fixed issue with restrict to in notebooks. --- stochss/handlers/util/stochss_notebook.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/stochss/handlers/util/stochss_notebook.py b/stochss/handlers/util/stochss_notebook.py index 0ce4ec11e6..36cd304ded 100644 --- a/stochss/handlers/util/stochss_notebook.py +++ b/stochss/handlers/util/stochss_notebook.py @@ -350,12 +350,12 @@ def __create_reaction_strings(self, model, pad, type_refs=None): reac_str = f'{pad}{reac["name"]} = Reaction(name="{reac["name"]}", ' reac_str += f'reactants={react_str}, products={prod_str}, ' if not reac['massaction']: - reac_str += f'propensity_function="{reac["propensity"]}"' - reac_str += f'odepropensity_function="{reac["odePropensity"]}"' + reac_str += f'propensity_function="{reac["propensity"]}", ' + reac_str += f'ode_propensity_function="{reac["odePropensity"]}"' else: reac_str += f'rate="{reac["rate"]["name"]}"' if self.s_model['is_spatial']: - types = [f"self.{type_refs[d_type]}" for d_type in reac["types"]] + types = [f"self.{type_refs[d_type].upper()}" for d_type in reac["types"]] if len(types) < len(type_refs): reac_str += f', restrict_to=[{", ".join(types)}]' reac_str += ")" @@ -402,7 +402,7 @@ def __create_species_strings(self, model, pad, type_refs=None): names.append(spec["name"]) spec_str = f'{pad}{spec["name"]} = Species(name="{spec["name"]}", ' if self.s_model['is_spatial']: - types = [f"self.{type_refs[d_type]}" for d_type in spec['types']] + types = [f"self.{type_refs[d_type].upper()}" for d_type in spec['types']] spec_str += f"diffusion_coefficient={spec['diffusionConst']}, " spec_str += f"restrict_to=[{', '.join(types)}])" else: From 4dfeebba9eda9690deab842df7ebe4c8572b1bfd Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Thu, 18 Aug 2022 11:25:55 -0400 Subject: [PATCH 4/9] Updated the 3D cylinder demo example. --- .../3D-Cylinder.proj/3D-Cylinder.domn | 1 + .../3D_cylinder.wkgp/3D_cylinder.ipynb | 199 ++++++++++++++++++ .../3D_cylinder.wkgp/3D_cylinder.smdl | 1 + public_models/3D_Cylinder/3D_cylinder.domn | 1 - public_models/3D_Cylinder/3D_cylinder.ipynb | 1 - public_models/3D_Cylinder/3D_cylinder.smdl | 1 - 6 files changed, 201 insertions(+), 3 deletions(-) create mode 100644 public_models/3D-Cylinder.proj/3D-Cylinder.domn create mode 100644 public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.ipynb create mode 100644 public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.smdl delete mode 100644 public_models/3D_Cylinder/3D_cylinder.domn delete mode 100644 public_models/3D_Cylinder/3D_cylinder.ipynb delete mode 100644 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\ No newline at end of file diff --git a/public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.ipynb b/public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.ipynb new file mode 100644 index 0000000000..acd805f528 --- /dev/null +++ b/public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3D Cylinder Demo\n", + "***\n", + "This is an example of a 3D reaction diffusion system.\n", + "\n", + "- The domain is a cylinder\n", + "- Species A is created at the right end of the cylinder\n", + "- Species B is created at the left end of the cylinder\n", + "- Diffusion occurs throughout the volume of the cylinder\n", + "- A and B are destroyed on contact\n", + "- Deterministic and Stochastic simulations are run\n", + "\n", + "This example allows us to check if reaction diffusion dynamics are accurate.\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import spatialpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the 3D Cylinder Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_3D_cylinder():\n", + " model = spatialpy.Model(\"3D_cylinder\")\n", + " \n", + " # Define Domain Type IDs as constants of the Model\n", + " model.MIDDLE = 'Middle'\n", + " model.EDGE1 = 'Edge1'\n", + " model.EDGE2 = 'Edge2'\n", + " \n", + " # Domain\n", + " domain = spatialpy.Domain.read_stochss_domain('3D_cylinder.smdl')\n", + " model.add_domain(domain)\n", + "\n", + " model.staticDomain = True\n", + "\n", + " # Variables\n", + " A = spatialpy.Species(name=\"A\", diffusion_coefficient=0.1, restrict_to=[model.MIDDLE, model.EDGE1])\n", + " B = spatialpy.Species(name=\"B\", diffusion_coefficient=0.1, restrict_to=[model.MIDDLE, model.EDGE2])\n", + " model.add_species([A, B])\n", + "\n", + " # Parameters\n", + " k_react = spatialpy.Parameter(name=\"k_react\", expression=\"1\")\n", + " k_creat1 = spatialpy.Parameter(name=\"k_creat1\", expression=\"100\")\n", + " k_creat2 = spatialpy.Parameter(name=\"k_creat2\", expression=\"100\")\n", + " left = spatialpy.Parameter(name=\"left\", expression=\"0.5092013833059308\")\n", + " right = spatialpy.Parameter(name=\"right\", expression=\"0.505804729089437\")\n", + " model.add_parameter([k_react, k_creat1, k_creat2, left, right])\n", + "\n", + " # Reactions\n", + " r1 = spatialpy.Reaction(\n", + " name=\"r1\", restrict_to=[model.EDGE1],\n", + " reactants={}, products={'A': 1},\n", + " propensity_function=\"k_creat1 / left * vol\",\n", + " ode_propensity_function=\"k_creat1\"\n", + " )\n", + " r2 = spatialpy.Reaction(\n", + " name=\"r2\", restrict_to=[model.EDGE2],\n", + " reactants={}, products={'B': 1},\n", + " propensity_function=\"k_creat2 / right * vol\",\n", + " ode_propensity_function=\"k_creat2\"\n", + " )\n", + " r3 = spatialpy.Reaction(\n", + " name=\"r3\", rate=\"k_react\",\n", + " reactants={'A': 1, 'B': 1}, products={}\n", + " )\n", + " model.add_reaction([r1, r2, r3])\n", + "\n", + " # Timespan\n", + "# tspan = spatialpy.TimeSpan.arange(1, t=500, timestep_size=1e-3)\n", + " tspan = spatialpy.TimeSpan.arange(1, t=500, timestep_size=1)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_3D_cylinder()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('A', animated=True, width=None, height=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.smdl b/public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.smdl new file mode 100644 index 0000000000..c846e784c5 --- /dev/null +++ b/public_models/3D-Cylinder.proj/3D_cylinder.wkgp/3D_cylinder.smdl @@ -0,0 +1 @@ +{"is_spatial":true,"defaultID":11,"defaultMode":"discrete","annotation":"This is an example of a 3D reaction diffusion system.\n- The domain is a cylinder\n- Species A is created at the right end of the cylinder\n- Species B is created at the left end of the cylinder\n- Diffusion occurs throughout the volume of the cylinder\n- A and B are destroyed on contact\n- Deterministic and Stochastic simulations are run\n\nThis example allows us to check if reaction diffusion dynamics are accurate.","volume":1,"template_version":1,"modelSettings":{"endSim":500,"timeStep":1,"timestepSize":0.001},"domain":{"boundary_condition":{"reflect_x":true,"reflect_y":true,"reflect_z":true},"c_0":10,"gravity":[0,0,0],"p_0":100,"rho_0":1,"size":null,"x_lim":[-5,5],"y_lim":[-1,1],"z_lim":[-1,1],"static":true,"template_version":1,"types":[{"c":10,"fixed":false,"geometry":"","mass":1,"name":"Un-Assigned","nu":0,"rho":1,"typeID":0,"volume":1},{"c":10,"fixed":false,"geometry":"abs(x + 5) >= 0.05","mass":1,"name":"Middle","nu":0,"rho":1,"typeID":1,"volume":1},{"c":10,"fixed":false,"geometry":"abs(x - 5) < 0.05","mass":1,"name":"Edge1","nu":0,"rho":1,"typeID":2,"volume":1},{"c":10,"fixed":false,"geometry":"abs(x + 5) < 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"Edge2", "typeID": 3, "mass": 1.0, "volume": 1.0, "nu": 0.0, "fixed": false}]} diff --git a/public_models/3D_Cylinder/3D_cylinder.ipynb b/public_models/3D_Cylinder/3D_cylinder.ipynb deleted file mode 100644 index b9d21b09ff..0000000000 --- a/public_models/3D_Cylinder/3D_cylinder.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2\n","\n","import numpy as np\n","import spatialpy\n","from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n"," PlaceInitialCondition, \\\n"," UniformInitialCondition, \\\n"," ScatterInitialCondition"]},{"cell_type":"markdown","metadata":{},"source":["# 3D_cylinder"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["class M3Dcylinder(Model):\n"," def __init__(self):\n"," Model.__init__(self, name=\"3D_cylinder\")\n","\n"," # Domain\n"," mesh = Mesh.read_stochss_domain('3D_cylinder.smdl')\n"," self.add_mesh(mesh)\n","\n"," self.staticDomain = True\n","\n"," # Variables\n"," A = Species(name=\"A\", diffusion_constant=0.1)\n"," B = Species(name=\"B\", diffusion_constant=0.1)\n"," self.add_species([A, B])\n","\n"," self.restrict(A, [1, 2])\n"," self.restrict(B, [1, 3])\n","\n"," # Parameters\n"," k_react = Parameter(name=\"k_react\", expression=\"1\")\n"," k_creat1 = Parameter(name=\"k_creat1\", expression=\"196.38595510240296\")\n"," k_creat2 = Parameter(name=\"k_creat2\", expression=\"197.70475491603773\")\n"," self.add_parameter([k_react, k_creat1, k_creat2])\n","\n"," # Reactions\n"," r1 = Reaction(name=\"r1\", restrict_to=[2], reactants={}, products={B: 1}, rate=self.listOfParameters[\"k_creat1\"])\n"," r2 = Reaction(name=\"r2\", restrict_to=[3], reactants={}, products={B: 1}, rate=self.listOfParameters[\"k_creat2\"])\n"," r3 = Reaction(name=\"r3\", reactants={B: 2}, products={}, rate=self.listOfParameters[\"k_react\"])\n"," self.add_reaction([r1, r2, r3])\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 500, 1), timestep_size=1)"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["model = M3Dcylinder()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," kwargs = {\n"," # \"number_of_trajectories\":1,\n"," # \"seed\":None,\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plot_species('A', animated=True, width=None, height=None)"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/3D_Cylinder/3D_cylinder.smdl b/public_models/3D_Cylinder/3D_cylinder.smdl deleted file mode 100644 index 740aa54412..0000000000 --- a/public_models/3D_Cylinder/3D_cylinder.smdl +++ /dev/null @@ -1 +0,0 @@ -{"annotation":"SpatialPy 3D Cylinder Demo Model","defaultID":9,"defaultMode":"discrete","domain":{"boundary_condition":{"reflect_x":true,"reflect_y":true,"reflect_z":true},"c_0":10,"gravity":[0, 0, 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B","types":[3]},{"annotation":"","compID":8,"massaction":true,"name":"r3","products":[],"propensity":"","rate":{"compID":3,"expression":1,"name":"k_react"},"reactants":[{"ratio":1,"specie":{"annotation":"","compID":1,"diffusionCoeff":0.1,"isSwitchTol":true,"mode":"discrete","name":"A","switchMin":100,"switchTol":0.03,"types":[1,2],"value":0}},{"ratio":1,"specie":{"annotation":"","compID":2,"diffusionCoeff":0.1,"isSwitchTol":true,"mode":"discrete","name":"B","switchMin":100,"switchTol":0.03,"types":[1,3],"value":0}}],"reactionType":"custom-massaction","summary":"A + B \\rightarrow \\emptyset","types":[1,2,3]}],"rules":[],"species":[{"annotation":"","compID":1,"diffusionCoeff":0.1,"isSwitchTol":true,"mode":"discrete","name":"A","switchMin":100,"switchTol":0.03,"types":[1,2],"value":0,"diffusionConst":0.1},{"annotation":"","compID":2,"diffusionCoeff":0.1,"isSwitchTol":true,"mode":"discrete","name":"B","switchMin":100,"switchTol":0.03,"types":[1,3],"value":0,"diffusionConst":0.1}],"volume":1,"name":"cylinder_demo3d"} From 1597db5691f61e34b29b50a0772b5e01fe8a48df Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Thu, 18 Aug 2022 19:14:10 -0400 Subject: [PATCH 5/9] Fixed display error for spatial modes. --- client/model-view/model-view.js | 2 +- stochss/handlers/util/stochss_spatial_model.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/client/model-view/model-view.js b/client/model-view/model-view.js index 8cdd074563..13f8c11a02 100644 --- a/client/model-view/model-view.js +++ b/client/model-view/model-view.js @@ -79,7 +79,7 @@ module.exports = View.extend({ this.setReadOnlyMode("model-mode"); this.setReadOnlyMode("system-volume"); }else { - if(this.model.defaultMode === ""){ + if(this.model.defaultMode === "" && !this.model.is_spatial){ if(this.model.is_spatial) { this.model.defaultMode = "discrete"; $(this.queryByHook("spatial-discrete")).prop('checked', true); diff --git a/stochss/handlers/util/stochss_spatial_model.py b/stochss/handlers/util/stochss_spatial_model.py index 9f45ec33dd..9ce57b9e7b 100644 --- a/stochss/handlers/util/stochss_spatial_model.py +++ b/stochss/handlers/util/stochss_spatial_model.py @@ -59,6 +59,7 @@ def __init__(self, path, new=False, model=None): if new: if model is None: model = self.get_model_template() + model['defaultMode'] = "discrete" if not model['is_spatial']: model['is_spatial'] = True if isinstance(model, str): From 7b95add671aec48460bc3c7f4b10a36cf1c82a0d Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Fri, 19 Aug 2022 09:21:11 -0400 Subject: [PATCH 6/9] Updated the gillespy2 examples. --- .../Brusselator.sbml | 14 +- .../Brusselator.wkgp/Brusselator.ipynb | 193 ++++ .../Brusselator.wkgp/Brusselator.mdl | 1 + public_models/Brusselator/Brusselator.ipynb | 1 - public_models/Brusselator/Brusselator.mdl | 443 -------- .../Degradation.sbml | 4 +- .../Degradation.wkgp/Degradation.ipynb | 163 +++ .../Degradation.wkgp/Degradation.mdl | 1 + public_models/Degradation/Degradation.ipynb | 1 - public_models/Degradation/Degradation.mdl | 84 -- .../Dimerization.sbml | 23 +- .../Dimerization.wkgp/Dimerization.ipynb | 176 ++++ .../Dimerization.wkgp/Dimerization.mdl | 1 + public_models/Dimerization/Dimerization.ipynb | 1 - public_models/Dimerization/Dimerization.mdl | 183 ---- .../Genetic_Toggle_Switch.sbml} | 26 +- .../Genetic_Toggle_Switch.ipynb | 186 ++++ .../Genetic_Toggle_Switch.mdl | 1 + .../Toggle_Switch_1D_Parameter_Sweep.ipynb | 368 +++++++ .../Toggle_Switch_2D_Parameter_Sweep.ipynb | 381 +++++++ ...ggle_Switch_SCIOPE_Model_Exploration.ipynb | 359 +++++++ ...Toggle_Switch_SCIOPE_Model_Inference.ipynb | 396 ++++++++ .../Lotkavolterra_Oscillator.sbml | 18 +- .../Lotkavolterra_Oscillator.ipynb | 174 ++++ .../Lotkavolterra_Oscillator.mdl | 1 + .../Lotkavolterra_Oscillator.ipynb | 1 - .../Lotkavolterra_Oscillator.mdl | 1 - .../Michaelis_Menten.sbml | 80 ++ .../Michaelis_Menten.ipynb | 176 ++++ .../Michaelis_Menten.mdl | 1 + .../Michaelis_Menten/Michaelis_Menten.ipynb | 1 - .../Michaelis_Menten/Michaelis_Menten.mdl | 332 ------ .../Michaelis_Menten/Michaelis_Menten.sbml | 76 -- .../{Opioid => Opioid.proj}/Opioid.sbml | 14 +- .../Opioid.proj/Opioid.wkgp/Opioid.ipynb | 223 ++++ .../Opioid.proj/Opioid.wkgp/Opioid.mdl | 1 + public_models/Opioid/Opioid.ipynb | 1 - public_models/Opioid/Opioid.mdl | 953 ------------------ .../Oregonator.sbml | 77 +- .../Oregonator.wkgp/Oregonator.ipynb | 189 ++++ .../Oregonator.wkgp/Oregonator.mdl | 1 + public_models/Oregonator/Oregonator.ipynb | 1 - public_models/Oregonator/Oregonator.mdl | 567 ----------- .../{Schlogl => Schlogl.proj}/Schlogl.sbml | 34 +- .../Schlogl.proj/Schlogl.wkgp/Schlogl.ipynb | 173 ++++ .../Schlogl.proj/Schlogl.wkgp/Schlogl.mdl | 1 + public_models/Schlogl/Schlogl.ipynb | 1 - public_models/Schlogl/Schlogl.mdl | 249 ----- .../Toggle_Switch/Toggle_Switch.ipynb | 1 - public_models/Toggle_Switch/Toggle_Switch.mdl | 233 ----- .../Toggle_Switch_1D_Parameter_Sweep.ipynb | 1 - .../Toggle_Switch_2D_Parameter_Sweep.ipynb | 1 - ...ggle_Switch_SCIOPE_Model_Exploration.ipynb | 1 - ...Toggle_Switch_SCIOPE_Model_Inference.ipynb | 1 - .../Tyson_2_State_Oscillator.sbml} | 8 +- .../Tyson_2_State_Oscillator.ipynb | 192 ++++ .../Tyson_2_State_Oscillator.mdl | 1 + .../Tyson_Oscillator/Tyson_Oscillator.ipynb | 1 - .../Tyson_Oscillator/Tyson_Oscillator.mdl | 322 ------ .../Vilar_Oscillator.sbml | 0 .../Vilar_Oscillator.ipynb | 250 +++++ .../Vilar_Oscillator.mdl | 1 + ..._Oscillator_SCIOPE_Model_Exploration.ipynb | 423 ++++++++ ...ar_Oscillator_SCIOPE_Model_Inference.ipynb | 460 +++++++++ .../Vilar_Oscillator/Vilar_Oscillator.ipynb | 152 --- .../Vilar_Oscillator/Vilar_Oscillator.mdl | 1 - ...lar_OscillatorSciopeModelExploration.ipynb | 330 ------ ...Vilar_OscillatorSciopeModelInference.ipynb | 374 ------- 68 files changed, 4705 insertions(+), 4400 deletions(-) rename public_models/{Brusselator => Brusselator.proj}/Brusselator.sbml (81%) create mode 100644 public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.ipynb create mode 100644 public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.mdl delete mode 100644 public_models/Brusselator/Brusselator.ipynb delete mode 100644 public_models/Brusselator/Brusselator.mdl rename public_models/{Degradation => Degradation.proj}/Degradation.sbml (78%) create mode 100644 public_models/Degradation.proj/Degradation.wkgp/Degradation.ipynb create mode 100644 public_models/Degradation.proj/Degradation.wkgp/Degradation.mdl delete mode 100644 public_models/Degradation/Degradation.ipynb delete mode 100644 public_models/Degradation/Degradation.mdl rename public_models/{Dimerization => Dimerization.proj}/Dimerization.sbml (66%) create mode 100644 public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.ipynb create mode 100644 public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.mdl delete mode 100644 public_models/Dimerization/Dimerization.ipynb delete mode 100644 public_models/Dimerization/Dimerization.mdl rename public_models/{Toggle_Switch/Toggle_Switch.sbml => Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.sbml} (72%) create mode 100644 public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.ipynb create mode 100644 public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.mdl create mode 100644 public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_1D_Parameter_Sweep.ipynb create mode 100644 public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_2D_Parameter_Sweep.ipynb create mode 100644 public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Exploration.ipynb create mode 100644 public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Inference.ipynb rename public_models/{Lotkavolterra_Oscillator => Lotkavolterra-Oscillator.proj}/Lotkavolterra_Oscillator.sbml (77%) create mode 100644 public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.ipynb create mode 100644 public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.mdl delete mode 100644 public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.ipynb delete mode 100644 public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.mdl create mode 100644 public_models/Michaelis-Menten.proj/Michaelis_Menten.sbml create mode 100644 public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.ipynb create mode 100644 public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.mdl delete mode 100644 public_models/Michaelis_Menten/Michaelis_Menten.ipynb delete mode 100644 public_models/Michaelis_Menten/Michaelis_Menten.mdl delete mode 100644 public_models/Michaelis_Menten/Michaelis_Menten.sbml rename public_models/{Opioid => Opioid.proj}/Opioid.sbml (88%) create mode 100644 public_models/Opioid.proj/Opioid.wkgp/Opioid.ipynb create mode 100644 public_models/Opioid.proj/Opioid.wkgp/Opioid.mdl delete mode 100644 public_models/Opioid/Opioid.ipynb delete mode 100644 public_models/Opioid/Opioid.mdl rename public_models/{Oregonator => Oregonator.proj}/Oregonator.sbml (67%) create mode 100644 public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.ipynb create mode 100644 public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.mdl delete mode 100644 public_models/Oregonator/Oregonator.ipynb delete mode 100644 public_models/Oregonator/Oregonator.mdl rename public_models/{Schlogl => Schlogl.proj}/Schlogl.sbml (58%) create mode 100644 public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.ipynb create mode 100644 public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.mdl delete mode 100644 public_models/Schlogl/Schlogl.ipynb delete mode 100644 public_models/Schlogl/Schlogl.mdl delete mode 100644 public_models/Toggle_Switch/Toggle_Switch.ipynb delete mode 100644 public_models/Toggle_Switch/Toggle_Switch.mdl delete mode 100644 public_models/Toggle_Switch/Toggle_Switch_1D_Parameter_Sweep.ipynb delete mode 100644 public_models/Toggle_Switch/Toggle_Switch_2D_Parameter_Sweep.ipynb delete mode 100644 public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Exploration.ipynb delete mode 100644 public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Inference.ipynb rename public_models/{Tyson_Oscillator/Tyson_Oscillator.sbml => Tyson-Oscillator.proj/Tyson_2_State_Oscillator.sbml} (90%) create mode 100644 public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.ipynb create mode 100644 public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.mdl delete mode 100644 public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb delete mode 100644 public_models/Tyson_Oscillator/Tyson_Oscillator.mdl rename public_models/{Vilar_Oscillator => Vilar-Oscillator.proj}/Vilar_Oscillator.sbml (100%) create mode 100644 public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.ipynb create mode 100644 public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.mdl create mode 100644 public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Exploration.ipynb create mode 100644 public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Inference.ipynb delete mode 100644 public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb delete mode 100644 public_models/Vilar_Oscillator/Vilar_Oscillator.mdl delete mode 100644 public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelExploration.ipynb delete mode 100644 public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelInference.ipynb diff --git a/public_models/Brusselator/Brusselator.sbml b/public_models/Brusselator.proj/Brusselator.sbml similarity index 81% rename from public_models/Brusselator/Brusselator.sbml rename to public_models/Brusselator.proj/Brusselator.sbml index 3380c80229..78ce6dd466 100644 --- a/public_models/Brusselator/Brusselator.sbml +++ b/public_models/Brusselator.proj/Brusselator.sbml @@ -2,15 +2,15 @@ - + - - - - - - + + + + + + diff --git a/public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.ipynb b/public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.ipynb new file mode 100644 index 0000000000..8b0e6f07b9 --- /dev/null +++ b/public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.ipynb @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Brusselator\n", + "***\n", + "https://en.wikipedia.org/wiki/Brusselator\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Brusselator Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_brusselator(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Brusselator\")\n", + " model.volume = 1000\n", + "\n", + " # Variables\n", + " A = gillespy2.Species(name=\"A\", initial_value=100000, mode=\"discrete\")\n", + " B = gillespy2.Species(name=\"B\", initial_value=100000, mode=\"discrete\")\n", + " C = gillespy2.Species(name=\"C\", initial_value=0, mode=\"discrete\")\n", + " D = gillespy2.Species(name=\"D\", initial_value=0, mode=\"discrete\")\n", + " X = gillespy2.Species(name=\"X\", initial_value=2000, mode=\"discrete\")\n", + " Y = gillespy2.Species(name=\"Y\", initial_value=1000, mode=\"discrete\")\n", + " model.add_species([A, B, C, D, X, Y])\n", + "\n", + " # Parameters\n", + " rate1 = gillespy2.Parameter(name=\"rate1\", expression=\"5000\")\n", + " rate2 = gillespy2.Parameter(name=\"rate2\", expression=\"50\")\n", + " rate3 = gillespy2.Parameter(name=\"rate3\", expression=\"5e-05\")\n", + " rate4 = gillespy2.Parameter(name=\"rate4\", expression=\"5\")\n", + " model.add_parameter([rate1, rate2, rate3, rate4])\n", + "\n", + " # Reactions\n", + " reaction1 = gillespy2.Reaction(\n", + " name=\"reaction1\",\n", + " reactants={'A': 1}, products={'X': 1, 'A': 1},\n", + " propensity_function=\"rate1\",\n", + " ode_propensity_function=\"rate1\"\n", + " )\n", + " reaction2 = gillespy2.Reaction(\n", + " name=\"reaction2\",\n", + " reactants={'B': 1, 'X': 1}, products={'Y': 1, 'C': 1, 'B': 1},\n", + " propensity_function=\"rate2*X\",\n", + " ode_propensity_function=\"rate2*X\"\n", + " )\n", + " reaction3 = gillespy2.Reaction(\n", + " name=\"reaction3\",\n", + " reactants={'X': 2, 'Y': 1}, products={'X': 3},\n", + " propensity_function=\"rate3*Y*X*(X-1)/2\",\n", + " ode_propensity_function=\"rate3*Y*X*(X-1)/2\"\n", + " )\n", + " reaction4 = gillespy2.Reaction(\n", + " name=\"reaction4\",\n", + " reactants={'X': 1}, products={'D': 1},\n", + " propensity_function=\"rate4*X\",\n", + " ode_propensity_function=\"rate4*X\"\n", + " )\n", + " model.add_reaction([reaction1, reaction2, reaction3, reaction4])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(0.01, t=30)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_brusselator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.mdl b/public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.mdl new file mode 100644 index 0000000000..d84d1e5203 --- /dev/null +++ b/public_models/Brusselator.proj/Brusselator.wkgp/Brusselator.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":15,"defaultMode":"discrete","annotation":"https://en.wikipedia.org/wiki/Brusselator","volume":1000,"modelSettings":{"endSim":30,"timeStep":0.01},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"A","value":100000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"B","value":100000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":3,"name":"C","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":4,"name":"D","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":5,"name":"X","value":2000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":6,"name":"Y","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":7,"name":"rate1","expression":5000,"annotation":""},{"compID":8,"name":"rate2","expression":50,"annotation":""},{"compID":9,"name":"rate3","expression":0.00005,"annotation":""},{"compID":10,"name":"rate4","expression":5,"annotation":""}],"reactions":[{"compID":11,"name":"reaction1","massaction":false,"odePropensity":"rate1","propensity":"rate1","annotation":"","rate":{},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"A","value":100000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":5,"name":"X","value":2000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":1,"name":"A","value":100000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":12,"name":"reaction2","massaction":false,"odePropensity":"rate2*X","propensity":"rate2*X","annotation":"","rate":{},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"B","value":100000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":5,"name":"X","value":2000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":6,"name":"Y","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":3,"name":"C","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":2,"name":"B","value":100000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":13,"name":"reaction3","massaction":false,"odePropensity":"rate3*Y*X*(X-1)/2","propensity":"rate3*Y*X*(X-1)/2","annotation":"","rate":{},"reactants":[{"ratio":2,"specie":{"compID":5,"name":"X","value":2000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":6,"name":"Y","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":3,"specie":{"compID":5,"name":"X","value":2000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":14,"name":"reaction4","massaction":false,"odePropensity":"rate4*X","propensity":"rate4*X","annotation":"","rate":{},"reactants":[{"ratio":1,"specie":{"compID":5,"name":"X","value":2000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":4,"name":"D","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Brusselator/Brusselator.ipynb b/public_models/Brusselator/Brusselator.ipynb deleted file mode 100644 index 5f0e2539dd..0000000000 --- a/public_models/Brusselator/Brusselator.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Brusselator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Brusselator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Brusselator\")\n"," self.volume = 1000\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"rate1\", expression=\"5000\"))\n"," self.add_parameter(Parameter(name=\"rate2\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"rate3\", expression=\"5e-05\"))\n"," self.add_parameter(Parameter(name=\"rate4\", expression=\"5\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=100000, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=100000, mode=\"discrete\"))\n"," self.add_species(Species(name=\"C\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"D\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"X\", initial_value=2000, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Y\", initial_value=1000, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"reaction1\", reactants={'A': 1}, products={'X': 1, 'A': 1}, propensity_function=\"rate1\"))\n"," self.add_reaction(Reaction(name=\"reaction2\", reactants={'B': 1, 'X': 1}, products={'Y': 1, 'C': 1, 'B': 1}, propensity_function=\"rate2*X\"))\n"," self.add_reaction(Reaction(name=\"reaction3\", reactants={'X': 2, 'Y': 1}, products={'X': 3}, propensity_function=\"rate3*Y*X*(X-1)/2\"))\n"," self.add_reaction(Reaction(name=\"reaction4\", reactants={'X': 1}, products={'D': 1}, propensity_function=\"rate4*X\"))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 30, 0.01))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Brusselator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Brusselator/Brusselator.mdl b/public_models/Brusselator/Brusselator.mdl deleted file mode 100644 index e6140ae132..0000000000 --- a/public_models/Brusselator/Brusselator.mdl +++ /dev/null @@ -1,443 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 15, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 30, - "timeStep": 0.01, - "volume": 1000 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "A", - "value": 100000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "B", - "value": 100000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 3, - "name": "C", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 4, - "name": "D", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 5, - "name": "X", - "value": 2000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 6, - "name": "Y", - "value": 1000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 7, - "name": "rate1", - "expression": "5000", - "annotation": "" - }, - { - "compID": 8, - "name": "rate2", - "expression": "50", - "annotation": "" - }, - { - "compID": 9, - "name": "rate3", - "expression": "0.00005", - "annotation": "" - }, - { - "compID": 10, - "name": "rate4", - "expression": "5", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 11, - "name": "reaction1", - "reactionType": "custom-propensity", - "summary": "A \\rightarrow X+A", - "massaction": false, - "propensity": "rate1", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "A", - "value": 100000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "X", - "value": 2000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "A", - "value": 100000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 12, - "name": "reaction2", - "reactionType": "custom-propensity", - "summary": "B+X \\rightarrow Y+C+B", - "massaction": false, - "propensity": "rate2*X", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "B", - "value": 100000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "X", - "value": 2000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 6, - "name": "Y", - "value": 1000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "C", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "B", - "value": 100000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 13, - "name": "reaction3", - "reactionType": "custom-propensity", - "summary": "2X+Y \\rightarrow 3X", - "massaction": false, - "propensity": "rate3*Y*X*(X-1)/2", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [ - { - "ratio": 2, - "specie": { - "compID": 5, - "name": "X", - "value": 2000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 6, - "name": "Y", - "value": 1000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 3, - "specie": { - "compID": 5, - "name": "X", - "value": 2000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 14, - "name": "reaction4", - "reactionType": "custom-propensity", - "summary": "X \\rightarrow D", - "massaction": false, - "propensity": "rate4*X", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "X", - "value": 2000, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "D", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Degradation/Degradation.sbml b/public_models/Degradation.proj/Degradation.sbml similarity index 78% rename from public_models/Degradation/Degradation.sbml rename to public_models/Degradation.proj/Degradation.sbml index fd73d5258a..e26d56db8c 100644 --- a/public_models/Degradation/Degradation.sbml +++ b/public_models/Degradation.proj/Degradation.sbml @@ -2,10 +2,10 @@ - + - + diff --git a/public_models/Degradation.proj/Degradation.wkgp/Degradation.ipynb b/public_models/Degradation.proj/Degradation.wkgp/Degradation.ipynb new file mode 100644 index 0000000000..12632f28c4 --- /dev/null +++ b/public_models/Degradation.proj/Degradation.wkgp/Degradation.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Degradation\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Degradation Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_degradation(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Degradation\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " protein = gillespy2.Species(name=\"protein\", initial_value=50, mode=\"discrete\")\n", + " model.add_species([protein])\n", + "\n", + " # Parameters\n", + " decayrate = gillespy2.Parameter(name=\"decayrate\", expression=\"0.05\")\n", + " model.add_parameter([decayrate])\n", + "\n", + " # Reactions\n", + " reaction = gillespy2.Reaction(\n", + " name=\"reaction\", rate=\"decayrate\",\n", + " reactants={'protein': 1}, products={}\n", + " )\n", + " model.add_reaction([reaction])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=100)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_degradation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Degradation.proj/Degradation.wkgp/Degradation.mdl b/public_models/Degradation.proj/Degradation.wkgp/Degradation.mdl new file mode 100644 index 0000000000..9f6e977aaa --- /dev/null +++ b/public_models/Degradation.proj/Degradation.wkgp/Degradation.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":4,"defaultMode":"discrete","annotation":"","volume":1,"modelSettings":{"endSim":100,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"protein","value":50,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":2,"name":"decayrate","expression":0.05,"annotation":""}],"reactions":[{"compID":3,"name":"reaction","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":2,"name":"decayrate","expression":0.05,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"protein","value":50,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Degradation/Degradation.ipynb b/public_models/Degradation/Degradation.ipynb deleted file mode 100644 index 153133ff46..0000000000 --- a/public_models/Degradation/Degradation.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Degradation"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Degradation(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Degradation\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"decayrate\", expression=\"0.05\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"protein\", initial_value=50, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"reaction\", reactants={'protein': 1}, products={}, rate=self.listOfParameters[\"decayrate\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Degradation()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Degradation/Degradation.mdl b/public_models/Degradation/Degradation.mdl deleted file mode 100644 index f56b40c380..0000000000 --- a/public_models/Degradation/Degradation.mdl +++ /dev/null @@ -1,84 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 4, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 100, - "timeStep": 1, - "volume": 1 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "protein", - "value": 50, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 2, - "name": "decayrate", - "expression": "0.05", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 3, - "name": "reaction", - "reactionType": "destruction", - "summary": "protein \\rightarrow \\emptyset", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 2, - "name": "decayrate", - "expression": "0.05", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "protein", - "value": 50, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Dimerization/Dimerization.sbml b/public_models/Dimerization.proj/Dimerization.sbml similarity index 66% rename from public_models/Dimerization/Dimerization.sbml rename to public_models/Dimerization.proj/Dimerization.sbml index d1551b7c4e..8bf511886c 100644 --- a/public_models/Dimerization/Dimerization.sbml +++ b/public_models/Dimerization.proj/Dimerization.sbml @@ -2,11 +2,11 @@ - + - - + + @@ -23,10 +23,19 @@ - - k_c - Monomer - Monomer + + + + 0.5 + k_c + Monomer + + + Monomer + 1 + + + vol diff --git a/public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.ipynb b/public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.ipynb new file mode 100644 index 0000000000..2d206099c3 --- /dev/null +++ b/public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dimerization\n", + "***\n", + "This file contains a simple example to demonstrate the use of GillesPy2 to perform a stochastic simulation. The example is biologically-motivated; however, in terms of biology, it is overly simplistic and does not capture the real-life complexity of the process being modeled -- the aim is not biological realism but rather to illustrate basic usage of GillesPy2.\n", + "\n", + "Some molecules can self-associate to form structures known as dimers. \"Dimerization\" is a process in which two molecules of some molecular species (known as a \"monomer\" in this situation -- let's call it \"M\" for short) come together to create a new molecule (call it \"D\"), but do so in a way that is reversible, meaning the combined structure can also decay or dissociate back into \"M\". (More information about dimerization can be found in the online book \"Molecular Biology of the Cell\", 4th edition, at the site https://www.ncbi.nlm.nih.gov/books/NBK26830/.) A simple model of the dimerization process represents it as two reactions: a reaction in which one molecule of \"M\" reacts reversibly with another molecule of \"M\" to form one new molecule (call it \"D\"), and another reaction in which a molecule of \"D\" breaks apart into two molecules of \"M\". Each of these two reactions has its own rate. In terms of biochemical reactions, it looks like this:\n", + "\n", + "kc 2 M <----> D kd\n", + "where kc and kd represent the rate constants for creation and dissociation of the dimer, respectively.\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Dimerization Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_dimerization(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Dimerization\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " Monomer = gillespy2.Species(name=\"Monomer\", initial_value=30, mode=\"discrete\")\n", + " Dimer = gillespy2.Species(name=\"Dimer\", initial_value=0, mode=\"discrete\")\n", + " model.add_species([Monomer, Dimer])\n", + "\n", + " # Parameters\n", + " k_c = gillespy2.Parameter(name=\"k_c\", expression=\"0.005\")\n", + " k_d = gillespy2.Parameter(name=\"k_d\", expression=\"0.08\")\n", + " model.add_parameter([k_c, k_d])\n", + "\n", + " # Reactions\n", + " r_creation = gillespy2.Reaction(\n", + " name=\"r_creation\", rate=\"k_c\",\n", + " reactants={'Monomer': 2}, products={'Dimer': 1}\n", + " )\n", + " r_dissociation = gillespy2.Reaction(\n", + " name=\"r_dissociation\", rate=\"k_d\",\n", + " reactants={'Dimer': 1}, products={'Monomer': 2}\n", + " )\n", + " model.add_reaction([r_creation, r_dissociation])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=100)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_dimerization()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.mdl b/public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.mdl new file mode 100644 index 0000000000..c52cdd30a9 --- /dev/null +++ b/public_models/Dimerization.proj/Dimerization.wkgp/Dimerization.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":7,"defaultMode":"discrete","annotation":"This file contains a simple example to demonstrate the use of GillesPy2 to perform a stochastic simulation. The example is biologically-motivated; however, in terms of biology, it is overly simplistic and does not capture the real-life complexity of the process being modeled -- the aim is not biological realism but rather to illustrate basic usage of GillesPy2.\n\nSome molecules can self-associate to form structures known as dimers. \"Dimerization\" is a process in which two molecules of some molecular species (known as a \"monomer\" in this situation -- let's call it \"M\" for short) come together to create a new molecule (call it \"D\"), but do so in a way that is reversible, meaning the combined structure can also decay or dissociate back into \"M\". (More information about dimerization can be found in the online book \"Molecular Biology of the Cell\", 4th edition, at the site https://www.ncbi.nlm.nih.gov/books/NBK26830/.) A simple model of the dimerization process represents it as two reactions: a reaction in which one molecule of \"M\" reacts reversibly with another molecule of \"M\" to form one new molecule (call it \"D\"), and another reaction in which a molecule of \"D\" breaks apart into two molecules of \"M\". Each of these two reactions has its own rate. In terms of biochemical reactions, it looks like this:\n\nkc 2 M <----> D kd\nwhere kc and kd represent the rate constants for creation and dissociation of the dimer, respectively.","volume":1,"modelSettings":{"endSim":100,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"Monomer","value":30,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"Dimer","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":3,"name":"k_c","expression":0.005,"annotation":""},{"compID":4,"name":"k_d","expression":0.08,"annotation":""}],"reactions":[{"compID":5,"name":"r_creation","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":3,"name":"k_c","expression":0.005,"annotation":""},"reactants":[{"ratio":2,"specie":{"compID":1,"name":"Monomer","value":30,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":2,"name":"Dimer","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":6,"name":"r_dissociation","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":4,"name":"k_d","expression":0.08,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"Dimer","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":2,"specie":{"compID":1,"name":"Monomer","value":30,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Dimerization/Dimerization.ipynb b/public_models/Dimerization/Dimerization.ipynb deleted file mode 100644 index 088188e31b..0000000000 --- a/public_models/Dimerization/Dimerization.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Dimerization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Dimerization(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Dimerization\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"k_c\", expression=\"0.005\"))\n"," self.add_parameter(Parameter(name=\"k_d\", expression=\"0.08\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Monomer\", initial_value=30, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dimer\", initial_value=0, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r_creation\", reactants={'Monomer': 2}, products={'Dimer': 1}, rate=self.listOfParameters[\"k_c\"]))\n"," self.add_reaction(Reaction(name=\"r_dissociation\", reactants={'Dimer': 1}, products={'Monomer': 2}, rate=self.listOfParameters[\"k_d\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Dimerization()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Dimerization/Dimerization.mdl b/public_models/Dimerization/Dimerization.mdl deleted file mode 100644 index 9dc6434554..0000000000 --- a/public_models/Dimerization/Dimerization.mdl +++ /dev/null @@ -1,183 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 7, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 100, - "timeStep": 1, - "volume": 1 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "Monomer", - "value": 30, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "Dimer", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 3, - "name": "k_c", - "expression": "0.005", - "annotation": "" - }, - { - "compID": 4, - "name": "k_d", - "expression": "0.08", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 5, - "name": "r_creation", - "reactionType": "dimerization", - "summary": "2Monomer \\rightarrow Dimer", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 3, - "name": "k_c", - "expression": "0.005", - "annotation": "" - }, - "reactants": [ - { - "ratio": 2, - "specie": { - "compID": 1, - "name": "Monomer", - "value": 30, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Dimer", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 6, - "name": "r_dissociation", - "reactionType": "custom-massaction", - "summary": "Dimer \\rightarrow 2Monomer", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 4, - "name": "k_d", - "expression": "0.08", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Dimer", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 2, - "specie": { - "compID": 1, - "name": "Monomer", - "value": 30, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch.sbml b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.sbml similarity index 72% rename from public_models/Toggle_Switch/Toggle_Switch.sbml rename to public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.sbml index dd263aa432..cc31061438 100644 --- a/public_models/Toggle_Switch/Toggle_Switch.sbml +++ b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.sbml @@ -2,15 +2,15 @@ - + - - + + - - + + @@ -18,7 +18,7 @@ - + @@ -30,7 +30,7 @@ 1 - B + V beta @@ -40,7 +40,7 @@ - + @@ -52,7 +52,7 @@ 1 - A + U gamma @@ -62,28 +62,28 @@ - + mu - A + U - + mu - B + V diff --git a/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.ipynb b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.ipynb new file mode 100644 index 0000000000..8fe8813d09 --- /dev/null +++ b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Genetic Toggle Switch\n", + "***\n", + "Gardner et al. Nature (1999) 'Construction of a genetic toggle switch in Escherichia coli'\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Genetic Toggle Switch Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_genetic_toggle_switch(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Toggle_Switch\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " U = gillespy2.Species(name=\"U\", initial_value=10, mode=\"discrete\")\n", + " V = gillespy2.Species(name=\"V\", initial_value=10, mode=\"discrete\")\n", + " model.add_species([U, V])\n", + "\n", + " # Parameters\n", + " alpha1 = gillespy2.Parameter(name=\"alpha1\", expression=\"1\")\n", + " alpha2 = gillespy2.Parameter(name=\"alpha2\", expression=\"1\")\n", + " beta = gillespy2.Parameter(name=\"beta\", expression=\"2\")\n", + " gamma = gillespy2.Parameter(name=\"gamma\", expression=\"2\")\n", + " mu = gillespy2.Parameter(name=\"mu\", expression=\"1\")\n", + " model.add_parameter([alpha1, alpha2, beta, gamma, mu])\n", + "\n", + " # Reactions\n", + " cu = gillespy2.Reaction(\n", + " name=\"cu\",\n", + " reactants={}, products={'U': 1},\n", + " propensity_function=\"alpha1/(1+pow(V,beta))\",\n", + " ode_propensity_function=\"alpha1/(1+pow(V,beta))\"\n", + " )\n", + " cv = gillespy2.Reaction(\n", + " name=\"cv\",\n", + " reactants={}, products={'V': 1},\n", + " propensity_function=\"alpha2/(1+pow(U,gamma))\",\n", + " ode_propensity_function=\"alpha2/(1+pow(U,gamma))\"\n", + " )\n", + " du = gillespy2.Reaction(\n", + " name=\"du\", rate=\"mu\",\n", + " reactants={'U': 1}, products={}\n", + " )\n", + " dv = gillespy2.Reaction(\n", + " name=\"dv\", rate=\"mu\",\n", + " reactants={'V': 1}, products={}\n", + " )\n", + " model.add_reaction([cu, cv, du, dv])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=101)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_genetic_toggle_switch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.mdl b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.mdl new file mode 100644 index 0000000000..aaab08db64 --- /dev/null +++ b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Genetic_Toggle_Switch.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":12,"defaultMode":"discrete","annotation":"Gardner et al. Nature (1999) 'Construction of a genetic toggle switch in Escherichia coli'","volume":1,"modelSettings":{"endSim":100,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"U","value":10,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"V","value":10,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":3,"name":"alpha1","expression":1,"annotation":""},{"compID":4,"name":"alpha2","expression":1,"annotation":""},{"compID":5,"name":"beta","expression":2,"annotation":""},{"compID":6,"name":"gamma","expression":2,"annotation":""},{"compID":7,"name":"mu","expression":1,"annotation":""}],"reactions":[{"compID":8,"name":"cu","massaction":false,"odePropensity":"alpha1/(1+pow(V,beta))","propensity":"alpha1/(1+pow(V,beta))","annotation":"","rate":{},"reactants":[],"products":[{"ratio":1,"specie":{"compID":1,"name":"U","value":10,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":9,"name":"cv","massaction":false,"odePropensity":"alpha2/(1+pow(U,gamma))","propensity":"alpha2/(1+pow(U,gamma))","annotation":"","rate":{},"reactants":[],"products":[{"ratio":1,"specie":{"compID":2,"name":"V","value":10,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":10,"name":"du","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":7,"name":"mu","expression":1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"U","value":10,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[]},{"compID":11,"name":"dv","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":7,"name":"mu","expression":1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"V","value":10,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_1D_Parameter_Sweep.ipynb b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_1D_Parameter_Sweep.ipynb new file mode 100644 index 0000000000..02377df2b7 --- /dev/null +++ b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_1D_Parameter_Sweep.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Genetic Toggle Switch\n", + "***\n", + "Gardner et al. Nature (1999) 'Construction of a genetic toggle switch in Escherichia coli'\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "MatPlotLib and Plotly are used for creating custom visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plotly.offline import iplot\n", + "import plotly.graph_objs as go" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Genetic Toggle Switch Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_genetic_toggle_switch(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Toggle_Switch\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " U = gillespy2.Species(name=\"U\", initial_value=10, mode=\"discrete\")\n", + " V = gillespy2.Species(name=\"V\", initial_value=10, mode=\"discrete\")\n", + " model.add_species([U, V])\n", + "\n", + " # Parameters\n", + " alpha1 = gillespy2.Parameter(name=\"alpha1\", expression=\"1\")\n", + " alpha2 = gillespy2.Parameter(name=\"alpha2\", expression=\"1\")\n", + " beta = gillespy2.Parameter(name=\"beta\", expression=\"2\")\n", + " gamma = gillespy2.Parameter(name=\"gamma\", expression=\"2\")\n", + " mu = gillespy2.Parameter(name=\"mu\", expression=\"1\")\n", + " model.add_parameter([alpha1, alpha2, beta, gamma, mu])\n", + "\n", + " # Reactions\n", + " cu = gillespy2.Reaction(\n", + " name=\"cu\",\n", + " reactants={}, products={'U': 1},\n", + " propensity_function=\"alpha1/(1+pow(V,beta))\",\n", + " ode_propensity_function=\"alpha1/(1+pow(V,beta))\"\n", + " )\n", + " cv = gillespy2.Reaction(\n", + " name=\"cv\",\n", + " reactants={}, products={'V': 1},\n", + " propensity_function=\"alpha2/(1+pow(U,gamma))\",\n", + " ode_propensity_function=\"alpha2/(1+pow(U,gamma))\"\n", + " )\n", + " du = gillespy2.Reaction(\n", + " name=\"du\", rate=\"mu\",\n", + " reactants={'U': 1}, products={}\n", + " )\n", + " dv = gillespy2.Reaction(\n", + " name=\"dv\", rate=\"mu\",\n", + " reactants={'V': 1}, products={}\n", + " )\n", + " model.add_reaction([cu, cv, du, dv])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=101)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_genetic_toggle_switch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " solver = gillespy2.SSACSolver(model=model)\n", + " kwargs = {\n", + " \"solver\":solver,\n", + " \"number_of_trajectories\":20,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Post Processing\n", + "***\n", + "### Feature extraction function\n", + "What value(s) do you want to extract from the simulation trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def population_at_last_timepoint(c, res):\n", + " if c.verbose:\n", + " print(f'population_at_last_timepoint {c.variable_of_interest}={res[c.variable_of_interest][-1]}')\n", + " return res[c.variable_of_interest][-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregation function\n", + "How do we combine the values from multiple trajectores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def mean_std_of_ensemble(c, data):\n", + " a = numpy.average(data)\n", + " s = numpy.std(data)\n", + " if c.verbose:\n", + " print(f'mean_std_of_ensemble m:{a} s:{s}')\n", + " return (a, s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Parameter Sweep\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterSweep1D():\n", + "\n", + " def run(c, kwargs, verbose=False):\n", + " c.verbose = verbose\n", + " fn = c.feature_extraction\n", + " ag = c.ensemble_aggragator\n", + " data = numpy.zeros((len(c.p1_range), 2)) # mean and std\n", + " for i, v1 in enumerate(c.p1_range):\n", + " if c.verbose:\n", + " print(f'running {c.p1}={v1}')\n", + " if(c.number_of_trajectories > 1):\n", + " tmp_results = model.run(**kwargs, variables={c.p1:v1})\n", + " (m, s) = ag([fn(x) for x in tmp_results])\n", + " data[i, 0] = m\n", + " data[i, 1] = s\n", + " else:\n", + " tmp_result = model.run(**kwargs, variables={c.p1:v1})\n", + " data[i, 0] = c.feature_extraction(tmp_result)\n", + " c.data = data\n", + "\n", + "\n", + " def plot(c):\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " plt.title(f'Parameter Sweep - Variable:{c.variable_of_interest}')\n", + " plt.errorbar(c.p1_range, c.data[:, 0], c.data[:, 1])\n", + " plt.xlabel(c.p1, fontsize=16, fontweight='bold')\n", + " plt.ylabel('Population', fontsize=16, fontweight='bold')\n", + "\n", + "\n", + " def plotplotly(c, return_plotly_figure=False):\n", + " visible = c.number_of_trajectories > 1\n", + " error_y = dict(type='data', array=c.data[:, 1], visible=visible)\n", + "\n", + " trace_list = [go.Scatter(x=c.p1_range, y=c.data[:, 0], error_y=error_y)]\n", + "\n", + " title = dict(text=f'Parameter Sweep - Variable: {c.variable_of_interest}', x=0.5)\n", + " yaxis_label = dict(title='Population')\n", + " xaxis_label = dict(title=f'{c.p1}')\n", + "\n", + " layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n", + "\n", + " fig = dict(data=trace_list, layout=layout)\n", + "\n", + " if return_plotly_figure:\n", + " return fig\n", + " iplot(fig)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterSweepConfig(ParameterSweep1D):\n", + " # What class defines the GillesPy2 model\n", + " model = create_genetic_toggle_switch()\n", + " # ENTER PARAMETER HERE\n", + " p1 = 'alpha1'\n", + " # ENTER START VALUE FOR P1 RANGE HERE\n", + " p1_min = 0.5 * float(eval(model.get_parameter(p1).expression))\n", + " # ENTER END VALUE FOR P1 RANGE HERE\n", + " p1_max = 1.5 * float(eval(model.get_parameter(p1).expression))\n", + " # ENTER THE NUMBER OF STEPS FOR P1 HERE\n", + " p1_steps = 11\n", + " p1_range = numpy.linspace(p1_min, p1_max, p1_steps)\n", + " # ENTER VARIABLE OF INTEREST HERE\n", + " variable_of_interest = 'U'\n", + " number_of_trajectories = 20\n", + " # What feature of the simulation are we examining\n", + " feature_extraction = population_at_last_timepoint\n", + " # for ensemble resutls: how do we aggreggate the values\n", + " ensemble_aggragator = mean_std_of_ensemble" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Parameter Sweep\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "ps = ParameterSweepConfig()\n", + "%time ps.run(kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ps.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ps.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_2D_Parameter_Sweep.ipynb b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_2D_Parameter_Sweep.ipynb new file mode 100644 index 0000000000..509dcd057f --- /dev/null +++ b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_2D_Parameter_Sweep.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Genetic Toggle Switch\n", + "***\n", + "Gardner et al. Nature (1999) 'Construction of a genetic toggle switch in Escherichia coli'\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "MatPlotLib and Plotly are used for creating custom visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plotly.offline import init_notebook_mode, iplot\n", + "import plotly.graph_objs as go" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Genetic Toggle Switch Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_genetic_toggle_switch(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Toggle_Switch\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " U = gillespy2.Species(name=\"U\", initial_value=10, mode=\"discrete\")\n", + " V = gillespy2.Species(name=\"V\", initial_value=10, mode=\"discrete\")\n", + " model.add_species([U, V])\n", + "\n", + " # Parameters\n", + " alpha1 = gillespy2.Parameter(name=\"alpha1\", expression=\"1\")\n", + " alpha2 = gillespy2.Parameter(name=\"alpha2\", expression=\"1\")\n", + " beta = gillespy2.Parameter(name=\"beta\", expression=\"2\")\n", + " gamma = gillespy2.Parameter(name=\"gamma\", expression=\"2\")\n", + " mu = gillespy2.Parameter(name=\"mu\", expression=\"1\")\n", + " model.add_parameter([alpha1, alpha2, beta, gamma, mu])\n", + "\n", + " # Reactions\n", + " cu = gillespy2.Reaction(\n", + " name=\"cu\",\n", + " reactants={}, products={'U': 1},\n", + " propensity_function=\"alpha1/(1+pow(V,beta))\",\n", + " ode_propensity_function=\"alpha1/(1+pow(V,beta))\"\n", + " )\n", + " cv = gillespy2.Reaction(\n", + " name=\"cv\",\n", + " reactants={}, products={'V': 1},\n", + " propensity_function=\"alpha2/(1+pow(U,gamma))\",\n", + " ode_propensity_function=\"alpha2/(1+pow(U,gamma))\"\n", + " )\n", + " du = gillespy2.Reaction(\n", + " name=\"du\", rate=\"mu\",\n", + " reactants={'U': 1}, products={}\n", + " )\n", + " dv = gillespy2.Reaction(\n", + " name=\"dv\", rate=\"mu\",\n", + " reactants={'V': 1}, products={}\n", + " )\n", + " model.add_reaction([cu, cv, du, dv])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=101)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_genetic_toggle_switch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " solver = gillespy2.SSACSolver(model=model)\n", + " kwargs = {\n", + " \"solver\":solver,\n", + " \"number_of_trajectories\":20,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Post Processing\n", + "***\n", + "### Feature extraction function\n", + "What value(s) do you want to extract from the simulation trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def population_at_last_timepoint(c, res):\n", + " if c.verbose:\n", + " print(f'population_at_last_timepoint {c.variable_of_interest}={res[c.variable_of_interest][-1]}')\n", + " return res[c.variable_of_interest][-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregation function\n", + "How do we combine the values from multiple trajectores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def average_of_ensemble(c, data):\n", + " a = numpy.average(data)\n", + " if c.verbose:\n", + " print(f'average_of_ensemble = {a}')\n", + " return a" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Parameter Sweep\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterSweep2D():\n", + "\n", + " def run(c, kwargs, verbose=False):\n", + " c.verbose = verbose\n", + " fn = c.feature_extraction\n", + " ag = c.ensemble_aggragator\n", + " data = numpy.zeros((len(c.p1_range), len(c.p2_range)))\n", + " for i, v1 in enumerate(c.p1_range):\n", + " for j, v2 in enumerate(c.p2_range):\n", + " if c.verbose:\n", + " print(f'running {c.p1}={v1}, {c.p2}={v2}')\n", + " if(c.number_of_trajectories > 1):\n", + " tmp_results = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n", + " data[i, j] = ag([fn(x) for x in tmp_results])\n", + " else:\n", + " tmp_result = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n", + " data[i, j] = c.feature_extraction(tmp_result)\n", + " c.data = data\n", + "\n", + "\n", + " def plot(c):\n", + " fig, ax = plt.subplots(figsize=(8, 8))\n", + " plt.imshow(c.data)\n", + " ax.set_xticks(numpy.arange(c.data.shape[1]) + 0.5, minor=False)\n", + " ax.set_yticks(numpy.arange(c.data.shape[0]) + 0.5, minor=False)\n", + " plt.title(f'Parameter Sweep - Variable: {c.variable_of_interest}')\n", + " ax.set_xticklabels(c.p1_range, minor=False, rotation=90)\n", + " ax.set_yticklabels(c.p2_range, minor=False)\n", + " ax.set_xlabel(c.p1, fontsize=16, fontweight='bold')\n", + " ax.set_ylabel(c.p2, fontsize=16, fontweight='bold')\n", + " divider = make_axes_locatable(ax)\n", + " cax = divider.append_axes('right', size='5%', pad=0.2)\n", + " _ = plt.colorbar(ax=ax, cax=cax)\n", + "\n", + "\n", + " def plotplotly(c, return_plotly_figure=False):\n", + " xaxis_ticks = c.p1_range\n", + " yaxis_ticks = c.p2_range\n", + "\n", + " trace_list = [go.Heatmap(z=c.data, x=xaxis_ticks, y=yaxis_ticks)]\n", + " title = dict(text=f'Parameter Sweep - Variable: {c.variable_of_interest}', x=0.5)\n", + " xaxis_label = dict(title=f'{c.p1}')\n", + " yaxis_label = dict(title=f'{c.p2}')\n", + "\n", + " layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n", + "\n", + " fig = dict(data=trace_list, layout=layout)\n", + "\n", + " if return_plotly_figure:\n", + " return fig\n", + " iplot(fig)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ParameterSweepConfig(ParameterSweep2D):\n", + " # What class defines the GillesPy2 model\n", + " model = create_genetic_toggle_switch()\n", + " # ENTER PARAMETER 1 HERE\n", + " p1 = 'alpha1'\n", + " # ENTER PARAMETER 2 HERE\n", + " p2 = 'alpha2'\n", + " # ENTER START VALUE FOR P1 RANGE HERE\n", + " p1_min = 0.5 * float(eval(model.get_parameter(p1).expression))\n", + " # ENTER END VALUE FOR P1 RANGE HERE\n", + " p1_max = 1.5 * float(eval(model.get_parameter(p1).expression))\n", + " # ENTER THE NUMBER OF STEPS FOR P1 HERE\n", + " p1_steps = 11\n", + " p1_range = numpy.linspace(p1_min, p1_max, p1_steps)\n", + " # ENTER START VALUE FOR P2 RANGE HERE\n", + " p2_min = 0.5 * float(eval(model.get_parameter(p2).expression))\n", + " # ENTER END VALUE FOR P2 RANGE HERE\n", + " p2_max = 1.5 * float(eval(model.get_parameter(p2).expression))\n", + " # ENTER THE NUMBER OF STEPS FOR P2 HERE\n", + " p2_steps = 11\n", + " p2_range = numpy.linspace(p2_min, p2_max, p2_steps)\n", + " # ENTER VARIABLE OF INTEREST HERE\n", + " variable_of_interest = 'U'\n", + " number_of_trajectories = 20\n", + " # What feature of the simulation are we examining\n", + " feature_extraction = population_at_last_timepoint\n", + " # for ensemble resutls: how do we aggreggate the values\n", + " ensemble_aggragator = average_of_ensemble" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Parameter Sweep\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "ps = ParameterSweepConfig()\n", + "%time ps.run(kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ps.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ps.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Exploration.ipynb b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Exploration.ipynb new file mode 100644 index 0000000000..6ff268f78f --- /dev/null +++ b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Exploration.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Genetic Toggle Switch\n", + "***\n", + "Gardner et al. Nature (1999) 'Construction of a genetic toggle switch in Escherichia coli'\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "from dask.distributed import Client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sciope.utilities.gillespy2 import wrapper\n", + "from sciope.designs import latin_hypercube_sampling\n", + "from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n", + "from sciope.stochmet.stochmet import StochMET\n", + "from sciope.models.label_propagation import LPModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Genetic Toggle Switch Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_genetic_toggle_switch(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Toggle_Switch\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " U = gillespy2.Species(name=\"U\", initial_value=10, mode=\"discrete\")\n", + " V = gillespy2.Species(name=\"V\", initial_value=10, mode=\"discrete\")\n", + " model.add_species([U, V])\n", + "\n", + " # Parameters\n", + " alpha1 = gillespy2.Parameter(name=\"alpha1\", expression=\"1\")\n", + " alpha2 = gillespy2.Parameter(name=\"alpha2\", expression=\"1\")\n", + " beta = gillespy2.Parameter(name=\"beta\", expression=\"2\")\n", + " gamma = gillespy2.Parameter(name=\"gamma\", expression=\"2\")\n", + " mu = gillespy2.Parameter(name=\"mu\", expression=\"1\")\n", + " model.add_parameter([alpha1, alpha2, beta, gamma, mu])\n", + "\n", + " # Reactions\n", + " cu = gillespy2.Reaction(\n", + " name=\"cu\",\n", + " reactants={}, products={'U': 1},\n", + " propensity_function=\"alpha1/(1+pow(V,beta))\",\n", + " ode_propensity_function=\"alpha1/(1+pow(V,beta))\"\n", + " )\n", + " cv = gillespy2.Reaction(\n", + " name=\"cv\",\n", + " reactants={}, products={'V': 1},\n", + " propensity_function=\"alpha2/(1+pow(U,gamma))\",\n", + " ode_propensity_function=\"alpha2/(1+pow(U,gamma))\"\n", + " )\n", + " du = gillespy2.Reaction(\n", + " name=\"du\", rate=\"mu\",\n", + " reactants={'U': 1}, products={}\n", + " )\n", + " dv = gillespy2.Reaction(\n", + " name=\"dv\", rate=\"mu\",\n", + " reactants={'V': 1}, products={}\n", + " )\n", + " model.add_reaction([cu, cv, du, dv])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=101)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_genetic_toggle_switch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " solver = gillespy2.SSACSolver(model=model)\n", + " kwargs = {\n", + " \"solver\":solver,\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Model Exploration\n", + "***\n", + "### Define simulator function (using gillespy2 wrapper)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "settings = configure_simulation()\n", + "simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['U', 'V'])\n", + "expression_array = wrapper.get_parameter_expression_array(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start local cluster using dask client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = Client()\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define parameter sampler/design and summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n", + "lhc.generate_array(1000) # creates a LHD of size 1000\n", + "\n", + "# will use default minimal set of features\n", + "summary_stats = SummariesTSFRESH()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initiate StochMET" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met = StochMET(simulator, lhc, summary_stats)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run parameter sweep\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.compute(n_points=500, chunk_size=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Explore the result\n", + "***\n", + "First lets add some appropiate information about the model and features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n", + "met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n", + "met.data.configurations['listOfSummaries'] = met.summaries.features\n", + "met.data.configurations['timepoints'] = model.tspan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use UMAP for dimension reduction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.explore(dr_method='umap')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here lets use the dimension reduction embedding as input data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = met.dr_model.embedding_\n", + "\n", + "model_lp = LPModel()\n", + "# train using basinhopping\n", + "model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just to vislualize the result we will map the label distribution to the user_labels (will enable us to see the LP model output when using method 'explore')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "user_labels = numpy.copy(met.data.user_labels)\n", + "# takes the label corresponding to index 0\n", + "met.data.user_labels = model_lp.model.label_distributions_[:, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.explore(dr_method='umap')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.data.user_labels = user_labels" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Inference.ipynb b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Inference.ipynb new file mode 100644 index 0000000000..fed14a91b9 --- /dev/null +++ b/public_models/Genetic-Toggle-Switch.proj/Genetic_Toggle_Switch.wkgp/Toggle_Switch_SCIOPE_Model_Inference.ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Genetic Toggle Switch\n", + "***\n", + "Gardner et al. Nature (1999) 'Construction of a genetic toggle switch in Escherichia coli'\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "from tsfresh.feature_extraction.settings import MinimalFCParameters\n", + "from dask.distributed import Client\n", + "from sklearn.metrics import mean_absolute_error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sciope.utilities.priors import uniform_prior\n", + "from sciope.utilities.summarystats import auto_tsfresh\n", + "from sciope.utilities.distancefunctions import naive_squared\n", + "from sciope.inference.abc_inference import ABC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Genetic Toggle Switch Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_genetic_toggle_switch(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Toggle_Switch\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " U = gillespy2.Species(name=\"U\", initial_value=10, mode=\"discrete\")\n", + " V = gillespy2.Species(name=\"V\", initial_value=10, mode=\"discrete\")\n", + " model.add_species([U, V])\n", + "\n", + " # Parameters\n", + " alpha1 = gillespy2.Parameter(name=\"alpha1\", expression=\"1\")\n", + " alpha2 = gillespy2.Parameter(name=\"alpha2\", expression=\"1\")\n", + " beta = gillespy2.Parameter(name=\"beta\", expression=\"2\")\n", + " gamma = gillespy2.Parameter(name=\"gamma\", expression=\"2\")\n", + " mu = gillespy2.Parameter(name=\"mu\", expression=\"1\")\n", + " model.add_parameter([alpha1, alpha2, beta, gamma, mu])\n", + "\n", + " # Reactions\n", + " cu = gillespy2.Reaction(\n", + " name=\"cu\",\n", + " reactants={}, products={'U': 1},\n", + " propensity_function=\"alpha1/(1+pow(V,beta))\",\n", + " ode_propensity_function=\"alpha1/(1+pow(V,beta))\"\n", + " )\n", + " cv = gillespy2.Reaction(\n", + " name=\"cv\",\n", + " reactants={}, products={'V': 1},\n", + " propensity_function=\"alpha2/(1+pow(U,gamma))\",\n", + " ode_propensity_function=\"alpha2/(1+pow(U,gamma))\"\n", + " )\n", + " du = gillespy2.Reaction(\n", + " name=\"du\", rate=\"mu\",\n", + " reactants={'U': 1}, products={}\n", + " )\n", + " dv = gillespy2.Reaction(\n", + " name=\"dv\", rate=\"mu\",\n", + " reactants={'V': 1}, products={}\n", + " )\n", + " model.add_reaction([cu, cv, du, dv])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=101)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_genetic_toggle_switch()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " solver = gillespy2.SSACSolver(model=model)\n", + " kwargs = {\n", + " \"solver\":solver,\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Model Inference\n", + "***\n", + "### Generate some fixed(observed) data based on default parameters of the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "fixed_data = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reshape the data and remove timepoints array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fixed_data = fixed_data.to_array()\n", + "fixed_data = numpy.asarray([x.T for x in fixed_data])\n", + "fixed_data = fixed_data[:, 1:, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define prior distribution\n", + "Take default from mode 1 as reference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "default_param = numpy.array(list(model.listOfParameters.items()))[:, 1]\n", + "\n", + "bound = []\n", + "for exp in default_param:\n", + " bound.append(float(exp.expression))\n", + "\n", + "# Set the bounds\n", + "bound = numpy.array(bound)\n", + "dmin = bound * 0.1\n", + "dmax = bound * 2.0\n", + "\n", + "# Here we use uniform prior\n", + "uni_prior = uniform_prior.UniformPrior(dmin, dmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define simulator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_variables(params, model):\n", + " # params - array, need to have the same order as model.listOfParameters\n", + " variables = {}\n", + " for e, pname in enumerate(model.listOfParameters.keys()):\n", + " variables[pname] = params[e]\n", + " return variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the GillesPy2 Solver" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def simulator(params, model):\n", + " variables = get_variables(params, model)\n", + "\n", + " res = model.run(**kwargs, variables=variables)\n", + " res = res.to_array()\n", + " tot_res = numpy.asarray([x.T for x in res]) # reshape to (N, S, T)\n", + " # should not contain timepoints\n", + " tot_res = tot_res[:, 1:, :]\n", + "\n", + " return tot_res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wrapper, simulator function to abc should should only take one argument (the parameter point)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def simulator2(x):\n", + " return simulator(x, model=model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define summary statistics and distance function\n", + "Function to generate summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summ_func = auto_tsfresh.SummariesTSFRESH()\n", + "\n", + "# Distance\n", + "ns = naive_squared.NaiveSquaredDistance()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start local cluster using dask client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = Client()\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the abc instance\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abc = ABC(\n", + " fixed_data, sim=simulator2, prior_function=uni_prior,\n", + " summaries_function=summ_func.compute, distance_function=ns\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First compute the fixed(observed) mean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abc.compute_fixed_mean(chunk_size=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.sbml b/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.sbml similarity index 77% rename from public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.sbml rename to public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.sbml index f53cd3c81a..722c5873fd 100644 --- a/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.sbml +++ b/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.sbml @@ -2,11 +2,11 @@ - + - - + + @@ -42,10 +42,14 @@ - - k2 - R - W + + + + k2 + R + W + + vol diff --git a/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.ipynb b/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.ipynb new file mode 100644 index 0000000000..ba6d928f48 --- /dev/null +++ b/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lotkavolterra Oscillator\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Lotkavolterra Oscillator Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_lotkavolterra_oscillator(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Lotkavolterra_Oscillator\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " R = gillespy2.Species(name=\"R\", initial_value=0.0095, mode=\"continuous\")\n", + " W = gillespy2.Species(name=\"W\", initial_value=0.008, mode=\"continuous\")\n", + " model.add_species([R, W])\n", + "\n", + " # Parameters\n", + " k1 = gillespy2.Parameter(name=\"k1\", expression=\"0.004\")\n", + " k2 = gillespy2.Parameter(name=\"k2\", expression=\"0.5\")\n", + " k3 = gillespy2.Parameter(name=\"k3\", expression=\"0.0045\")\n", + " model.add_parameter([k1, k2, k3])\n", + "\n", + " # Reactions\n", + " r1 = gillespy2.Reaction(\n", + " name=\"r1\", rate=\"k1\",\n", + " reactants={'R': 1}, products={'R': 2}\n", + " )\n", + " r2 = gillespy2.Reaction(\n", + " name=\"r2\", rate=\"k2\",\n", + " reactants={'R': 1, 'W': 1}, products={'W': 2}\n", + " )\n", + " r3 = gillespy2.Reaction(\n", + " name=\"r3\", rate=\"k3\",\n", + " reactants={'W': 1}, products={}\n", + " )\n", + " model.add_reaction([r1, r2, r3])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=9000)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_lotkavolterra_oscillator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"Tau-Hybrid\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.mdl b/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.mdl new file mode 100644 index 0000000000..da0e3ce42e --- /dev/null +++ b/public_models/Lotkavolterra-Oscillator.proj/Lotkavolterra_Oscillator.wkgp/Lotkavolterra_Oscillator.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":10,"defaultMode":"continuous","annotation":"","volume":1,"modelSettings":{"endSim":9000,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":3,"name":"k1","expression":0.004,"annotation":""},{"compID":4,"name":"k2","expression":0.5,"annotation":""},{"compID":5,"name":"k3","expression":0.0045,"annotation":""}],"reactions":[{"compID":7,"name":"r1","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":3,"name":"k1","expression":0.004,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":2,"specie":{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":8,"name":"r2","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":4,"name":"k2","expression":0.5,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":2,"specie":{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":9,"name":"r3","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":5,"name":"k3","expression":0.0045,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.ipynb b/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.ipynb deleted file mode 100644 index d36610645c..0000000000 --- a/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Lotkavolterra_Oscillator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class LotkavolterraOscillator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Lotkavolterra_Oscillator\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"k1\", expression=\"0.004\"))\n"," self.add_parameter(Parameter(name=\"k2\", expression=\"0.5\"))\n"," self.add_parameter(Parameter(name=\"k3\", expression=\"0.0045\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"R\", initial_value=0.0095, mode=\"continuous\"))\n"," self.add_species(Species(name=\"W\", initial_value=0.008, mode=\"continuous\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'R': 1}, products={'R': 2}, rate=self.listOfParameters[\"k1\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'R': 1, 'W': 1}, products={'W': 2}, rate=self.listOfParameters[\"k2\"]))\n"," self.add_reaction(Reaction(name=\"r3\", reactants={'W': 1}, products={}, rate=self.listOfParameters[\"k3\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 9000, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = LotkavolterraOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," kwargs = {\n"," # \"solver\":TauHybridSolver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.mdl b/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.mdl deleted file mode 100644 index a55c07554d..0000000000 --- a/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.mdl +++ /dev/null @@ -1 +0,0 @@ -{"is_spatial":false,"defaultID":10,"defaultMode":"continuous","modelSettings":{"endSim":9000,"timeStep":1,"volume":1},"meshSettings":{"count":2},"species":[{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]},{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]}],"initialConditions":[],"parameters":[{"compID":3,"name":"k1","expression":"0.004","annotation":""},{"compID":4,"name":"k2","expression":"0.5","annotation":""},{"compID":5,"name":"k3","expression":"0.0045","annotation":""}],"reactions":[{"compID":7,"name":"r1","reactionType":"custom-massaction","summary":"R \\rightarrow 2R","massaction":true,"propensity":"","annotation":"","subdomains":["subdomain 1:","subdomain 2:"],"rate":{"compID":3,"name":"k1","expression":"0.004","annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]}}],"products":[{"ratio":2,"specie":{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]}}]},{"compID":8,"name":"r2","reactionType":"custom-massaction","summary":"R+W \\rightarrow 2W","massaction":true,"propensity":"","annotation":"","subdomains":["subdomain 1:","subdomain 2:"],"rate":{"compID":4,"name":"k2","expression":"0.5","annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"R","value":0.0095,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]}},{"ratio":1,"specie":{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]}}],"products":[{"ratio":2,"specie":{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]}}]},{"compID":9,"name":"r3","reactionType":"destruction","summary":"W \\rightarrow \\emptyset","massaction":true,"propensity":"","annotation":"","subdomains":["subdomain 1:","subdomain 2:"],"rate":{"compID":5,"name":"k3","expression":"0.0045","annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"W","value":0.008,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":"","diffusionCoeff":0,"subdomains":["subdomain 1:","subdomain 2:"]}}],"products":[]}],"rules":[],"eventsCollection":[],"functionDefinitions":[]} \ No newline at end of file diff --git a/public_models/Michaelis-Menten.proj/Michaelis_Menten.sbml b/public_models/Michaelis-Menten.proj/Michaelis_Menten.sbml new file mode 100644 index 0000000000..d62ff4641b --- /dev/null +++ b/public_models/Michaelis-Menten.proj/Michaelis_Menten.sbml @@ -0,0 +1,80 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + rate1 + Substrate + Enzyme + + vol + + + + + + + + + + + + + + + + + rate2 + Enzyme_Substrate_Complex + + + + + + + + + + + + + + + + + rate3 + Enzyme_Substrate_Complex + + + + + + + diff --git a/public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.ipynb b/public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.ipynb new file mode 100644 index 0000000000..88c188b6e3 --- /dev/null +++ b/public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Michaelis Menten\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Michaelis Menten Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_michaelis_menten(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Michaelis_Menten\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " Substrate = gillespy2.Species(name=\"Substrate\", initial_value=301, mode=\"discrete\")\n", + " Enzyme = gillespy2.Species(name=\"Enzyme\", initial_value=120, mode=\"discrete\")\n", + " Enzyme_Substrate_Complex = gillespy2.Species(name=\"Enzyme_Substrate_Complex\", initial_value=0, mode=\"discrete\")\n", + " Product = gillespy2.Species(name=\"Product\", initial_value=0, mode=\"discrete\")\n", + " model.add_species([Substrate, Enzyme, Enzyme_Substrate_Complex, Product])\n", + "\n", + " # Parameters\n", + " rate1 = gillespy2.Parameter(name=\"rate1\", expression=\"0.0017\")\n", + " rate2 = gillespy2.Parameter(name=\"rate2\", expression=\"0.5\")\n", + " rate3 = gillespy2.Parameter(name=\"rate3\", expression=\"0.1\")\n", + " model.add_parameter([rate1, rate2, rate3])\n", + "\n", + " # Reactions\n", + " r1 = gillespy2.Reaction(\n", + " name=\"r1\", rate=\"rate1\",\n", + " reactants={'Substrate': 1, 'Enzyme': 1}, products={'Enzyme_Substrate_Complex': 1}\n", + " )\n", + " r2 = gillespy2.Reaction(\n", + " name=\"r2\", rate=\"rate2\",\n", + " reactants={'Enzyme_Substrate_Complex': 1}, products={'Substrate': 1, 'Enzyme': 1}\n", + " )\n", + " r3 = gillespy2.Reaction(\n", + " name=\"r3\", rate=\"rate3\",\n", + " reactants={'Enzyme_Substrate_Complex': 1}, products={'Enzyme': 1, 'Product': 1}\n", + " )\n", + " model.add_reaction([r1, r2, r3])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=100)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_michaelis_menten()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.mdl b/public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.mdl new file mode 100644 index 0000000000..63a1105d19 --- /dev/null +++ b/public_models/Michaelis-Menten.proj/Michaelis_Menten.wkgp/Michaelis_Menten.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":11,"defaultMode":"discrete","annotation":"","volume":1,"modelSettings":{"endSim":100,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"Substrate","value":301,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"Enzyme","value":120,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":3,"name":"Enzyme_Substrate_Complex","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":4,"name":"Product","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":5,"name":"rate1","expression":0.0017,"annotation":""},{"compID":6,"name":"rate2","expression":0.5,"annotation":""},{"compID":7,"name":"rate3","expression":0.1,"annotation":""}],"reactions":[{"compID":8,"name":"r1","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":5,"name":"rate1","expression":0.0017,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"Substrate","value":301,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":2,"name":"Enzyme","value":120,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":3,"name":"Enzyme_Substrate_Complex","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":9,"name":"r2","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":6,"name":"rate2","expression":0.5,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":3,"name":"Enzyme_Substrate_Complex","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"Substrate","value":301,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":2,"name":"Enzyme","value":120,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":10,"name":"r3","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":7,"name":"rate3","expression":0.1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":3,"name":"Enzyme_Substrate_Complex","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":2,"name":"Enzyme","value":120,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":4,"name":"Product","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Michaelis_Menten/Michaelis_Menten.ipynb b/public_models/Michaelis_Menten/Michaelis_Menten.ipynb deleted file mode 100644 index 41730478d1..0000000000 --- a/public_models/Michaelis_Menten/Michaelis_Menten.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Michaelis_Menten"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class MichaelisMenten(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Michaelis_Menten\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"rate1\", expression=\"0.0017\"))\n"," self.add_parameter(Parameter(name=\"rate2\", expression=\"0.5\"))\n"," self.add_parameter(Parameter(name=\"rate3\", expression=\"0.1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=301, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=120, mode=\"discrete\"))\n"," self.add_species(Species(name=\"C\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"D\", initial_value=0, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'A': 1, 'B': 1}, products={'C': 1}, rate=self.listOfParameters[\"rate1\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'C': 1}, products={'A': 1, 'B': 1}, rate=self.listOfParameters[\"rate2\"]))\n"," self.add_reaction(Reaction(name=\"r3\", reactants={'C': 1}, products={'B': 1, 'D': 1}, rate=self.listOfParameters[\"rate3\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = MichaelisMenten()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Michaelis_Menten/Michaelis_Menten.mdl b/public_models/Michaelis_Menten/Michaelis_Menten.mdl deleted file mode 100644 index 30b766510b..0000000000 --- a/public_models/Michaelis_Menten/Michaelis_Menten.mdl +++ /dev/null @@ -1,332 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 11, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 100, - "timeStep": 1, - "volume": 1 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "A", - "value": 301, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "B", - "value": 120, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 3, - "name": "C", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 4, - "name": "D", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 5, - "name": "rate1", - "expression": "0.0017", - "annotation": "" - }, - { - "compID": 6, - "name": "rate2", - "expression": "0.5", - "annotation": "" - }, - { - "compID": 7, - "name": "rate3", - "expression": "0.1", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 8, - "name": "r1", - "reactionType": "merge", - "summary": "A+B \\rightarrow C", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 5, - "name": "rate1", - "expression": "0.0017", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "A", - "value": 301, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "B", - "value": 120, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "C", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 9, - "name": "r2", - "reactionType": "split", - "summary": "C \\rightarrow A+B", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 6, - "name": "rate2", - "expression": "0.5", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "C", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "A", - "value": 301, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "B", - "value": 120, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 10, - "name": "r3", - "reactionType": "split", - "summary": "C \\rightarrow B+D", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 7, - "name": "rate3", - "expression": "0.1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "C", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "B", - "value": 120, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "D", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Michaelis_Menten/Michaelis_Menten.sbml b/public_models/Michaelis_Menten/Michaelis_Menten.sbml deleted file mode 100644 index d130789b03..0000000000 --- a/public_models/Michaelis_Menten/Michaelis_Menten.sbml +++ /dev/null @@ -1,76 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - rate1 - A - B - - - - - - - - - - - - - - - - - rate2 - C - - - - - - - - - - - - - - - - - rate3 - C - - - - - - - diff --git a/public_models/Opioid/Opioid.sbml b/public_models/Opioid.proj/Opioid.sbml similarity index 88% rename from public_models/Opioid/Opioid.sbml rename to public_models/Opioid.proj/Opioid.sbml index 01d571bcb4..50a89fc647 100644 --- a/public_models/Opioid/Opioid.sbml +++ b/public_models/Opioid.proj/Opioid.sbml @@ -2,15 +2,15 @@ - + - - - - - - + + + + + + diff --git a/public_models/Opioid.proj/Opioid.wkgp/Opioid.ipynb b/public_models/Opioid.proj/Opioid.wkgp/Opioid.ipynb new file mode 100644 index 0000000000..8d1619a3c8 --- /dev/null +++ b/public_models/Opioid.proj/Opioid.wkgp/Opioid.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Opioid\n", + "***\n", + "Model based off the prescription opioid crisis in the United States. Based off a paper from the University of Tennessee https://0afa17f2-bd49-4985-b62b-358fb4a6bf3f.filesusr.com/ugd/f70b03_22c7703e4a3b4da6b9555c738ed8566d.pdf\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Opioid Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_opioid(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Opioid\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " Susceptible = gillespy2.Species(name=\"Susceptible\", initial_value=1000, mode=\"discrete\")\n", + " Prescribed_User = gillespy2.Species(name=\"Prescribed_User\", initial_value=0, mode=\"discrete\")\n", + " Addicted = gillespy2.Species(name=\"Addicted\", initial_value=0, mode=\"discrete\")\n", + " Rehab = gillespy2.Species(name=\"Rehab\", initial_value=0, mode=\"discrete\")\n", + " Natural_Deaths = gillespy2.Species(name=\"Natural_Deaths\", initial_value=0, mode=\"discrete\")\n", + " Addicted_Deaths = gillespy2.Species(name=\"Addicted_Deaths\", initial_value=0, mode=\"discrete\")\n", + " model.add_species([Susceptible, Prescribed_User, Addicted, Rehab, Natural_Deaths, Addicted_Deaths])\n", + "\n", + " # Parameters\n", + " alpha = gillespy2.Parameter(name=\"alpha\", expression=\"0.15\")\n", + " epsilon = gillespy2.Parameter(name=\"epsilon\", expression=\"0.8\")\n", + " beta_p = gillespy2.Parameter(name=\"beta_p\", expression=\"0.00266\")\n", + " beta_a = gillespy2.Parameter(name=\"beta_a\", expression=\"0.00094\")\n", + " gamma = gillespy2.Parameter(name=\"gamma\", expression=\"0.00744\")\n", + " zeta = gillespy2.Parameter(name=\"zeta\", expression=\"0.2\")\n", + " delta = gillespy2.Parameter(name=\"delta\", expression=\"0.1\")\n", + " sigma = gillespy2.Parameter(name=\"sigma\", expression=\"0.9\")\n", + " mu = gillespy2.Parameter(name=\"mu\", expression=\"0.00729\")\n", + " mu_prime = gillespy2.Parameter(name=\"mu_prime\", expression=\"0.01159\")\n", + " model.add_parameter([alpha, epsilon, beta_p, beta_a, gamma, zeta, delta, sigma, mu, mu_prime])\n", + "\n", + " # Reactions\n", + " SP = gillespy2.Reaction(\n", + " name=\"SP\", rate=\"alpha\",\n", + " reactants={'Susceptible': 1}, products={'Prescribed_User': 1}\n", + " )\n", + " SA_a = gillespy2.Reaction(\n", + " name=\"SA_a\", rate=\"beta_a\",\n", + " reactants={'Susceptible': 1}, products={'Addicted': 1}\n", + " )\n", + " SA_p = gillespy2.Reaction(\n", + " name=\"SA_p\", rate=\"beta_p\",\n", + " reactants={'Susceptible': 1}, products={'Addicted': 1}\n", + " )\n", + " PA = gillespy2.Reaction(\n", + " name=\"PA\", rate=\"gamma\",\n", + " reactants={'Prescribed_User': 1}, products={'Addicted': 1}\n", + " )\n", + " PS = gillespy2.Reaction(\n", + " name=\"PS\", rate=\"epsilon\",\n", + " reactants={'Prescribed_User': 1}, products={'Susceptible': 1}\n", + " )\n", + " AR = gillespy2.Reaction(\n", + " name=\"AR\", rate=\"zeta\",\n", + " reactants={'Addicted': 1}, products={'Rehab': 1}\n", + " )\n", + " RA = gillespy2.Reaction(\n", + " name=\"RA\", rate=\"delta\",\n", + " reactants={'Rehab': 1}, products={'Addicted': 1}\n", + " )\n", + " RS = gillespy2.Reaction(\n", + " name=\"RS\", rate=\"sigma\",\n", + " reactants={'Rehab': 1}, products={'Susceptible': 1}\n", + " )\n", + " mu_S = gillespy2.Reaction(\n", + " name=\"mu_S\", rate=\"mu\",\n", + " reactants={'Susceptible': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}\n", + " )\n", + " mu_P = gillespy2.Reaction(\n", + " name=\"mu_P\", rate=\"mu\",\n", + " reactants={'Prescribed_User': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}\n", + " )\n", + " mu_R = gillespy2.Reaction(\n", + " name=\"mu_R\", rate=\"mu\",\n", + " reactants={'Rehab': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}\n", + " )\n", + " mu_prime_A = gillespy2.Reaction(\n", + " name=\"mu_prime_A\", rate=\"mu_prime\",\n", + " reactants={'Addicted': 1}, products={'Susceptible': 1, 'Addicted_Deaths': 1}\n", + " )\n", + " model.add_reaction([SP, SA_a, SA_p, PA, PS, AR, RA, RS, mu_S, mu_P, mu_R, mu_prime_A])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=200)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_opioid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Opioid.proj/Opioid.wkgp/Opioid.mdl b/public_models/Opioid.proj/Opioid.wkgp/Opioid.mdl new file mode 100644 index 0000000000..206c982987 --- /dev/null +++ b/public_models/Opioid.proj/Opioid.wkgp/Opioid.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":29,"defaultMode":"discrete","annotation":"Model based off the prescription opioid crisis in the United States. Based off a paper from the University of Tennessee https://0afa17f2-bd49-4985-b62b-358fb4a6bf3f.filesusr.com/ugd/f70b03_22c7703e4a3b4da6b9555c738ed8566d.pdf","volume":1,"modelSettings":{"endSim":200,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"Prescribed_User","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":3,"name":"Addicted","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":4,"name":"Rehab","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":5,"name":"Natural_Deaths","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":6,"name":"Addicted_Deaths","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":7,"name":"alpha","expression":0.15,"annotation":""},{"compID":8,"name":"epsilon","expression":0.8,"annotation":""},{"compID":9,"name":"beta_p","expression":0.00266,"annotation":""},{"compID":10,"name":"beta_a","expression":0.00094,"annotation":""},{"compID":11,"name":"gamma","expression":0.00744,"annotation":""},{"compID":12,"name":"zeta","expression":0.2,"annotation":""},{"compID":13,"name":"delta","expression":0.1,"annotation":""},{"compID":14,"name":"sigma","expression":0.9,"annotation":""},{"compID":15,"name":"mu","expression":0.00729,"annotation":""},{"compID":16,"name":"mu_prime","expression":0.01159,"annotation":""}],"reactions":[{"compID":17,"name":"SP","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":7,"name":"alpha","expression":0.15,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":2,"name":"Prescribed_User","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":18,"name":"SA_a","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":10,"name":"beta_a","expression":0.00094,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":3,"name":"Addicted","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":19,"name":"SA_p","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":9,"name":"beta_p","expression":0.00266,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":3,"name":"Addicted","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":20,"name":"PA","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":11,"name":"gamma","expression":0.00744,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"Prescribed_User","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":3,"name":"Addicted","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":21,"name":"PS","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":8,"name":"epsilon","expression":0.8,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"Prescribed_User","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":22,"name":"AR","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":12,"name":"zeta","expression":0.2,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":3,"name":"Addicted","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":4,"name":"Rehab","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":23,"name":"RA","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":13,"name":"delta","expression":0.1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":4,"name":"Rehab","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":3,"name":"Addicted","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":24,"name":"RS","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":14,"name":"sigma","expression":0.9,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":4,"name":"Rehab","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":25,"name":"mu_S","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":15,"name":"mu","expression":0.00729,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":5,"name":"Natural_Deaths","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":26,"name":"mu_P","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":15,"name":"mu","expression":0.00729,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"Prescribed_User","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":5,"name":"Natural_Deaths","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":27,"name":"mu_R","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":15,"name":"mu","expression":0.00729,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":4,"name":"Rehab","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":5,"name":"Natural_Deaths","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":28,"name":"mu_prime_A","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":16,"name":"mu_prime","expression":0.01159,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":3,"name":"Addicted","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"Susceptible","value":1000,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":6,"name":"Addicted_Deaths","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Opioid/Opioid.ipynb b/public_models/Opioid/Opioid.ipynb deleted file mode 100644 index 1655876b54..0000000000 --- a/public_models/Opioid/Opioid.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Opioid"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Opioid(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Opioid\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha\", expression=\"0.15\"))\n"," self.add_parameter(Parameter(name=\"epsilon\", expression=\"0.8\"))\n"," self.add_parameter(Parameter(name=\"beta_p\", expression=\"0.00266\"))\n"," self.add_parameter(Parameter(name=\"beta_a\", expression=\"0.00094\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"0.00744\"))\n"," self.add_parameter(Parameter(name=\"zeta\", expression=\"0.2\"))\n"," self.add_parameter(Parameter(name=\"delta\", expression=\"0.1\"))\n"," self.add_parameter(Parameter(name=\"sigma\", expression=\"0.9\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"0.00729\"))\n"," self.add_parameter(Parameter(name=\"mu_prime\", expression=\"0.01159\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Susceptible\", initial_value=200, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Prescribed_User\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Addicted\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Rehab\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Natural_Deaths\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Addicted_Deaths\", initial_value=0, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"SP\", reactants={'Susceptible': 1}, products={'Prescribed_User': 1}, rate=self.listOfParameters[\"alpha\"]))\n"," self.add_reaction(Reaction(name=\"SA_a\", reactants={'Susceptible': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"beta_a\"]))\n"," self.add_reaction(Reaction(name=\"SA_p\", reactants={'Susceptible': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"beta_p\"]))\n"," self.add_reaction(Reaction(name=\"PA\", reactants={'Prescribed_User': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"gamma\"]))\n"," self.add_reaction(Reaction(name=\"PS\", reactants={'Prescribed_User': 1}, products={'Susceptible': 1}, rate=self.listOfParameters[\"epsilon\"]))\n"," self.add_reaction(Reaction(name=\"AR\", reactants={'Addicted': 1}, products={'Rehab': 1}, rate=self.listOfParameters[\"zeta\"]))\n"," self.add_reaction(Reaction(name=\"RA\", reactants={'Rehab': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"delta\"]))\n"," self.add_reaction(Reaction(name=\"RS\", reactants={'Rehab': 1}, products={'Susceptible': 1}, rate=self.listOfParameters[\"sigma\"]))\n"," self.add_reaction(Reaction(name=\"mu_S\", reactants={'Susceptible': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"mu_P\", reactants={'Prescribed_User': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"mu_R\", reactants={'Rehab': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"mu_prime_A\", reactants={'Addicted': 1}, products={'Susceptible': 1, 'Addicted_Deaths': 1}, rate=self.listOfParameters[\"mu_prime\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 200, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Opioid()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Opioid/Opioid.mdl b/public_models/Opioid/Opioid.mdl deleted file mode 100644 index 1b317f23cb..0000000000 --- a/public_models/Opioid/Opioid.mdl +++ /dev/null @@ -1,953 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 29, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 200, - "timeStep": 1, - "volume": 1 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "Prescribed_User", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 3, - "name": "Addicted", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 4, - "name": "Rehab", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 5, - "name": "Natural_Deaths", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 6, - "name": "Addicted_Deaths", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 7, - "name": "alpha", - "expression": "0.15", - "annotation": "" - }, - { - "compID": 8, - "name": "epsilon", - "expression": "0.8", - "annotation": "" - }, - { - "compID": 9, - "name": "beta_p", - "expression": "0.00266", - "annotation": "" - }, - { - "compID": 10, - "name": "beta_a", - "expression": "0.00094", - "annotation": "" - }, - { - "compID": 11, - "name": "gamma", - "expression": "0.00744", - "annotation": "" - }, - { - "compID": 12, - "name": "zeta", - "expression": "0.2", - "annotation": "" - }, - { - "compID": 13, - "name": "delta", - "expression": "0.1", - "annotation": "" - }, - { - "compID": 14, - "name": "sigma", - "expression": "0.9", - "annotation": "" - }, - { - "compID": 15, - "name": "mu", - "expression": "0.00729", - "annotation": "" - }, - { - "compID": 16, - "name": "mu_prime", - "expression": "0.01159", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 17, - "name": "SP", - "reactionType": "change", - "summary": "Susceptible \\rightarrow Prescribed\\_User", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 7, - "name": "alpha", - "expression": "0.15", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Prescribed_User", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 18, - "name": "SA_a", - "reactionType": "change", - "summary": "Susceptible \\rightarrow Addicted", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 10, - "name": "beta_a", - "expression": "0.00094", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "Addicted", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 19, - "name": "SA_p", - "reactionType": "change", - "summary": "Susceptibl \\rightarrow Addicted", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 9, - "name": "beta_p", - "expression": "0.00266", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "Addicted", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 20, - "name": "PA", - "reactionType": "change", - "summary": "Prescribed\\_User \\rightarrow Addicted", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 11, - "name": "gamma", - "expression": "0.00744", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Prescribed_User", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "Addicted", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 21, - "name": "PS", - "reactionType": "change", - "summary": "Prescribed\\_User \\rightarrow Susceptible", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 8, - "name": "epsilon", - "expression": "0.8", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Prescribed_User", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 22, - "name": "AR", - "reactionType": "change", - "summary": "Addicted \\rightarrow Rehab", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 12, - "name": "zeta", - "expression": "0.2", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "Addicted", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "Rehab", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 23, - "name": "RA", - "reactionType": "change", - "summary": "Rehab \\rightarrow Addicted", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 13, - "name": "delta", - "expression": "0.1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "Rehab", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "Addicted", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 24, - "name": "RS", - "reactionType": "change", - "summary": "Rehab \\rightarrow Susceptible", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 14, - "name": "sigma", - "expression": "0.9", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "Rehab", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 25, - "name": "mu_S", - "reactionType": "split", - "summary": "Susceptible \\rightarrow Susceptible+Natural\\_Deaths", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 15, - "name": "mu", - "expression": "0.00729", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "Natural_Deaths", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 26, - "name": "mu_P", - "reactionType": "split", - "summary": "Prescribed\\_User \\rightarrow Susceptible+Natural\\_Deaths", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 15, - "name": "mu", - "expression": "0.00729", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Prescribed_User", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "Natural_Deaths", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 27, - "name": "mu_R", - "reactionType": "split", - "summary": "Rehab \\rightarrow Susceptible+Natural\\_Deaths", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 15, - "name": "mu", - "expression": "0.00729", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "Rehab", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "Natural_Deaths", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 28, - "name": "mu_prime_A", - "reactionType": "split", - "summary": "Addicted \\rightarrow Susceptible+Addicted\\_Deaths", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 16, - "name": "mu_prime", - "expression": "0.01159", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "Addicted", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "Susceptible", - "value": 200, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 6, - "name": "Addicted_Deaths", - "value": 0, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Oregonator/Oregonator.sbml b/public_models/Oregonator.proj/Oregonator.sbml similarity index 67% rename from public_models/Oregonator/Oregonator.sbml rename to public_models/Oregonator.proj/Oregonator.sbml index c1caacc2d2..57d11a5459 100644 --- a/public_models/Oregonator/Oregonator.sbml +++ b/public_models/Oregonator.proj/Oregonator.sbml @@ -2,14 +2,14 @@ - + - - - - - + + + + + @@ -31,10 +31,14 @@ - - k1 - B - F + + + + k1 + B + F + + vol @@ -50,10 +54,14 @@ - - k2 - A - B + + + + k2 + A + B + + vol @@ -71,10 +79,14 @@ - - k3 - A - F + + + + k3 + A + F + + vol @@ -89,10 +101,19 @@ - - k4 - A - A + + + + 0.5 + k4 + A + + + A + 1 + + + vol @@ -109,10 +130,14 @@ - - k5 - C - F + + + + k5 + C + F + + vol diff --git a/public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.ipynb b/public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.ipynb new file mode 100644 index 0000000000..bcbe7bd336 --- /dev/null +++ b/public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Oregonator\n", + "***\n", + "http://www.scholarpedia.org/article/Oregonator\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Oregonator Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_oregonator(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Oregonator\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " F = gillespy2.Species(name=\"F\", initial_value=2, mode=\"continuous\")\n", + " A = gillespy2.Species(name=\"A\", initial_value=250, mode=\"continuous\")\n", + " B = gillespy2.Species(name=\"B\", initial_value=500, mode=\"continuous\")\n", + " C = gillespy2.Species(name=\"C\", initial_value=1000, mode=\"continuous\")\n", + " P = gillespy2.Species(name=\"P\", initial_value=0, mode=\"continuous\")\n", + " model.add_species([F, A, B, C, P])\n", + "\n", + " # Parameters\n", + " k1 = gillespy2.Parameter(name=\"k1\", expression=\"2\")\n", + " k2 = gillespy2.Parameter(name=\"k2\", expression=\"0.1\")\n", + " k3 = gillespy2.Parameter(name=\"k3\", expression=\"104\")\n", + " k4 = gillespy2.Parameter(name=\"k4\", expression=\"4e-07\")\n", + " k5 = gillespy2.Parameter(name=\"k5\", expression=\"26\")\n", + " model.add_parameter([k1, k2, k3, k4, k5])\n", + "\n", + " # Reactions\n", + " reaction1 = gillespy2.Reaction(\n", + " name=\"reaction1\", rate=\"k1\",\n", + " reactants={'B': 1, 'F': 1}, products={'A': 1, 'F': 1}\n", + " )\n", + " reaction2 = gillespy2.Reaction(\n", + " name=\"reaction2\", rate=\"k2\",\n", + " reactants={'A': 1, 'B': 1}, products={'P': 1}\n", + " )\n", + " reaction3 = gillespy2.Reaction(\n", + " name=\"reaction3\", rate=\"k3\",\n", + " reactants={'A': 1, 'F': 1}, products={'A': 2, 'C': 1, 'F': 1}\n", + " )\n", + " reaction4 = gillespy2.Reaction(\n", + " name=\"reaction4\", rate=\"k4\",\n", + " reactants={'A': 2}, products={'P': 1}\n", + " )\n", + " reaction5 = gillespy2.Reaction(\n", + " name=\"reaction5\", rate=\"k5\",\n", + " reactants={'C': 1, 'F': 1}, products={'B': 1, 'F': 1}\n", + " )\n", + " model.add_reaction([reaction1, reaction2, reaction3, reaction4, reaction5])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(0.1, t=5)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_oregonator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"Tau-Hybrid\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.mdl b/public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.mdl new file mode 100644 index 0000000000..d0d6b4c356 --- /dev/null +++ b/public_models/Oregonator.proj/Oregonator.wkgp/Oregonator.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":16,"defaultMode":"continuous","annotation":"http://www.scholarpedia.org/article/Oregonator","volume":1,"modelSettings":{"endSim":5,"timeStep":0.1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"F","value":2,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"A","value":250,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":3,"name":"B","value":500,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":4,"name":"C","value":1000,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":5,"name":"P","value":0,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":6,"name":"k1","expression":2,"annotation":""},{"compID":7,"name":"k2","expression":0.1,"annotation":""},{"compID":8,"name":"k3","expression":104,"annotation":""},{"compID":9,"name":"k4","expression":4e-7,"annotation":""},{"compID":10,"name":"k5","expression":26,"annotation":""}],"reactions":[{"compID":11,"name":"reaction1","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":6,"name":"k1","expression":2,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":3,"name":"B","value":500,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":1,"name":"F","value":2,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":2,"name":"A","value":250,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":1,"name":"F","value":2,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":12,"name":"reaction2","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":7,"name":"k2","expression":0.1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"A","value":250,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":3,"name":"B","value":500,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":5,"name":"P","value":0,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":13,"name":"reaction3","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":8,"name":"k3","expression":104,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"A","value":250,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":1,"name":"F","value":2,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":2,"specie":{"compID":2,"name":"A","value":250,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":4,"name":"C","value":1000,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":1,"name":"F","value":2,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":14,"name":"reaction4","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":9,"name":"k4","expression":4e-7,"annotation":""},"reactants":[{"ratio":2,"specie":{"compID":2,"name":"A","value":250,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":5,"name":"P","value":0,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":15,"name":"reaction5","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":10,"name":"k5","expression":26,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":4,"name":"C","value":1000,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":1,"name":"F","value":2,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":3,"name":"B","value":500,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":1,"name":"F","value":2,"mode":"continuous","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Oregonator/Oregonator.ipynb b/public_models/Oregonator/Oregonator.ipynb deleted file mode 100644 index 02563dae00..0000000000 --- a/public_models/Oregonator/Oregonator.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Oregonator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Oregonator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Oregonator\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"k1\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"k2\", expression=\"0.1\"))\n"," self.add_parameter(Parameter(name=\"k3\", expression=\"104\"))\n"," self.add_parameter(Parameter(name=\"k4\", expression=\"4e-07\"))\n"," self.add_parameter(Parameter(name=\"k5\", expression=\"26\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"F\", initial_value=2, mode=\"continuous\"))\n"," self.add_species(Species(name=\"A\", initial_value=250, mode=\"continuous\"))\n"," self.add_species(Species(name=\"B\", initial_value=500, mode=\"continuous\"))\n"," self.add_species(Species(name=\"C\", initial_value=1000, mode=\"continuous\"))\n"," self.add_species(Species(name=\"P\", initial_value=0, mode=\"continuous\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"reaction1\", reactants={'B': 1, 'F': 1}, products={'A': 1, 'F': 1}, rate=self.listOfParameters[\"k1\"]))\n"," self.add_reaction(Reaction(name=\"reaction2\", reactants={'A': 1, 'B': 1}, products={'P': 1}, rate=self.listOfParameters[\"k2\"]))\n"," self.add_reaction(Reaction(name=\"reaction3\", reactants={'A': 1, 'F': 1}, products={'A': 2, 'C': 1, 'F': 1}, rate=self.listOfParameters[\"k3\"]))\n"," self.add_reaction(Reaction(name=\"reaction4\", reactants={'A': 2}, products={'P': 1}, rate=self.listOfParameters[\"k4\"]))\n"," self.add_reaction(Reaction(name=\"reaction5\", reactants={'C': 1, 'F': 1}, products={'B': 1, 'F': 1}, rate=self.listOfParameters[\"k5\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 5, 0.1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Oregonator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," kwargs = {\n"," # \"solver\":TauHybridSolver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Oregonator/Oregonator.mdl b/public_models/Oregonator/Oregonator.mdl deleted file mode 100644 index 14e65bbac4..0000000000 --- a/public_models/Oregonator/Oregonator.mdl +++ /dev/null @@ -1,567 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 16, - "defaultMode": "continuous", - "modelSettings": { - "endSim": 5, - "timeStep": 0.1, - "volume": 1 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "F", - "value": 2, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "A", - "value": 250, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 3, - "name": "B", - "value": 500, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 4, - "name": "C", - "value": 1000, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 5, - "name": "P", - "value": 0, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 6, - "name": "k1", - "expression": "2", - "annotation": "" - }, - { - "compID": 7, - "name": "k2", - "expression": "0.1", - "annotation": "" - }, - { - "compID": 8, - "name": "k3", - "expression": "104", - "annotation": "" - }, - { - "compID": 9, - "name": "k4", - "expression": "4e-7", - "annotation": "" - }, - { - "compID": 10, - "name": "k5", - "expression": "26", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 11, - "name": "reaction1", - "reactionType": "four", - "summary": "B+F \\rightarrow A+F", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 6, - "name": "k1", - "expression": "2", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "B", - "value": 500, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "F", - "value": 2, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "A", - "value": 250, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "F", - "value": 2, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 12, - "name": "reaction2", - "reactionType": "merge", - "summary": "A+B \\rightarrow P", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 7, - "name": "k2", - "expression": "0.1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "A", - "value": 250, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "B", - "value": 500, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "P", - "value": 0, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 13, - "name": "reaction3", - "reactionType": "custom-massaction", - "summary": "A+F \\rightarrow 2A+C+F", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 8, - "name": "k3", - "expression": "104", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "A", - "value": 250, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "F", - "value": 2, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 2, - "specie": { - "compID": 2, - "name": "A", - "value": 250, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "C", - "value": 1000, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "F", - "value": 2, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 14, - "name": "reaction4", - "reactionType": "dimerization", - "summary": "2A \\rightarrow P", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 9, - "name": "k4", - "expression": "4e-7", - "annotation": "" - }, - "reactants": [ - { - "ratio": 2, - "specie": { - "compID": 2, - "name": "A", - "value": 250, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 5, - "name": "P", - "value": 0, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 15, - "name": "reaction5", - "reactionType": "four", - "summary": "C+F \\rightarrow B+F", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 10, - "name": "k5", - "expression": "26", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "C", - "value": 1000, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "F", - "value": 2, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "B", - "value": 500, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "F", - "value": 2, - "mode": "continuous", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Schlogl/Schlogl.sbml b/public_models/Schlogl.proj/Schlogl.sbml similarity index 58% rename from public_models/Schlogl/Schlogl.sbml rename to public_models/Schlogl.proj/Schlogl.sbml index a324264c3f..183c2884e0 100644 --- a/public_models/Schlogl/Schlogl.sbml +++ b/public_models/Schlogl.proj/Schlogl.sbml @@ -2,13 +2,13 @@ - + - - - - + + + + @@ -26,10 +26,14 @@ - - k1 - s1 - s4 + + + + k1 + s1 + s4 + + vol @@ -45,10 +49,14 @@ - - k2 - s2 - s4 + + + + k2 + s2 + s4 + + vol diff --git a/public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.ipynb b/public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.ipynb new file mode 100644 index 0000000000..d9675f0043 --- /dev/null +++ b/public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Schlogl\n", + "***\n", + "Schlogl F. Chemical reaction models for non-equilibrium phase transitions. Zeitschrift for Physik. 1972;253: 147–161. doi:10.1007/bf01379769\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Schlogl Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_schlogl(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Schlogl\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " s1 = gillespy2.Species(name=\"s1\", initial_value=300, mode=\"discrete\")\n", + " s2 = gillespy2.Species(name=\"s2\", initial_value=300, mode=\"discrete\")\n", + " s3 = gillespy2.Species(name=\"s3\", initial_value=300, mode=\"discrete\")\n", + " s4 = gillespy2.Species(name=\"s4\", initial_value=300, mode=\"discrete\")\n", + " model.add_species([s1, s2, s3, s4])\n", + "\n", + " # Parameters\n", + " k1 = gillespy2.Parameter(name=\"k1\", expression=\"1\")\n", + " k2 = gillespy2.Parameter(name=\"k2\", expression=\"1\")\n", + " model.add_parameter([k1, k2])\n", + "\n", + " # Reactions\n", + " r1 = gillespy2.Reaction(\n", + " name=\"r1\", rate=\"k1\",\n", + " reactants={'s1': 1, 's4': 1}, products={'s4': 2}\n", + " )\n", + " r2 = gillespy2.Reaction(\n", + " name=\"r2\", rate=\"k2\",\n", + " reactants={'s2': 1, 's4': 1}, products={'s3': 1}\n", + " )\n", + " model.add_reaction([r1, r2])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1000, t=100000)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_schlogl()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.mdl b/public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.mdl new file mode 100644 index 0000000000..535b5d988a --- /dev/null +++ b/public_models/Schlogl.proj/Schlogl.wkgp/Schlogl.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":9,"defaultMode":"discrete","annotation":"Schlogl F. Chemical reaction models for non-equilibrium phase transitions. Zeitschrift for Physik. 1972;253: 147–161. doi:10.1007/bf01379769","volume":1,"modelSettings":{"endSim":100000,"timeStep":1000},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"s1","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"s2","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":3,"name":"s3","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":4,"name":"s4","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":5,"name":"k1","expression":1,"annotation":""},{"compID":6,"name":"k2","expression":1,"annotation":""}],"reactions":[{"compID":7,"name":"r1","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":5,"name":"k1","expression":1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"s1","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":4,"name":"s4","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":2,"specie":{"compID":4,"name":"s4","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":8,"name":"r2","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":6,"name":"k2","expression":1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"s2","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":4,"name":"s4","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":3,"name":"s3","value":300,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Schlogl/Schlogl.ipynb b/public_models/Schlogl/Schlogl.ipynb deleted file mode 100644 index 7897e5931e..0000000000 --- a/public_models/Schlogl/Schlogl.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Schlogl"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Schlogl(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Schlogl\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"k1\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"k2\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"s1\", initial_value=300, mode=\"discrete\"))\n"," self.add_species(Species(name=\"s2\", initial_value=300, mode=\"discrete\"))\n"," self.add_species(Species(name=\"s3\", initial_value=300, mode=\"discrete\"))\n"," self.add_species(Species(name=\"s4\", initial_value=300, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'s1': 1, 's4': 1}, products={'s4': 2}, rate=self.listOfParameters[\"k1\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'s2': 1, 's4': 1}, products={'s3': 1}, rate=self.listOfParameters[\"k2\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100000, 1000))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Schlogl()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Schlogl/Schlogl.mdl b/public_models/Schlogl/Schlogl.mdl deleted file mode 100644 index c0db9c8284..0000000000 --- a/public_models/Schlogl/Schlogl.mdl +++ /dev/null @@ -1,249 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 9, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 100000, - "timeStep": 1000, - "volume": 1 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "s1", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "s2", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 3, - "name": "s3", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 4, - "name": "s4", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 5, - "name": "k1", - "expression": "1", - "annotation": "" - }, - { - "compID": 6, - "name": "k2", - "expression": "1", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 7, - "name": "r1", - "reactionType": "custom-massaction", - "summary": "s1+s4 \\rightarrow 2s4", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 5, - "name": "k1", - "expression": "1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "s1", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "s4", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 2, - "specie": { - "compID": 4, - "name": "s4", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 8, - "name": "r2", - "reactionType": "merge", - "summary": "s2+s4 \\rightarrow s3", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 6, - "name": "k2", - "expression": "1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "s2", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 4, - "name": "s4", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 3, - "name": "s3", - "value": 300, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch.ipynb b/public_models/Toggle_Switch/Toggle_Switch.ipynb deleted file mode 100644 index 11eba3b4fc..0000000000 --- a/public_models/Toggle_Switch/Toggle_Switch.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch.mdl b/public_models/Toggle_Switch/Toggle_Switch.mdl deleted file mode 100644 index 30b0714936..0000000000 --- a/public_models/Toggle_Switch/Toggle_Switch.mdl +++ /dev/null @@ -1,233 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 12, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 250, - "timeStep": 1, - "volume": 1 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "A", - "value": 2, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "B", - "value": 2, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 3, - "name": "alpha1", - "expression": "10", - "annotation": "" - }, - { - "compID": 4, - "name": "alpha2", - "expression": "10", - "annotation": "" - }, - { - "compID": 5, - "name": "beta", - "expression": "2", - "annotation": "" - }, - { - "compID": 6, - "name": "gamma", - "expression": "2", - "annotation": "" - }, - { - "compID": 7, - "name": "mu", - "expression": "1", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 8, - "name": "cu", - "reactionType": "custom-propensity", - "summary": "\\emptyset \\rightarrow A", - "massaction": false, - "propensity": "alpha1/(1+pow(B, beta))", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "A", - "value": 2, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 9, - "name": "cv", - "reactionType": "custom-propensity", - "summary": "\\emptyset \\rightarrow B", - "massaction": false, - "propensity": "alpha2/(1+pow(A, gamma))", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "B", - "value": 2, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 10, - "name": "du", - "reactionType": "destruction", - "summary": "A \\rightarrow \\emptyset", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 7, - "name": "mu", - "expression": "1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "A", - "value": 2, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [] - }, - { - "compID": 11, - "name": "dv", - "reactionType": "destruction", - "summary": "B \\rightarrow \\emptyset", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 7, - "name": "mu", - "expression": "1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "B", - "value": 2, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_1D_Parameter_Sweep.ipynb b/public_models/Toggle_Switch/Toggle_Switch_1D_Parameter_Sweep.ipynb deleted file mode 100644 index 9ce97948ff..0000000000 --- a/public_models/Toggle_Switch/Toggle_Switch_1D_Parameter_Sweep.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":20,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Post Processing"]},{"cell_type":"markdown","metadata":{},"source":["## Feature extraction function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# What value(s) do you want to extract from the simulation trajectory\n","def population_at_last_timepoint(c, res):\n"," if c.verbose:\n"," print(f'population_at_last_timepoint {c.variable_of_interest}={res[c.variable_of_interest][-1]}')\n"," return res[c.variable_of_interest][-1]"]},{"cell_type":"markdown","metadata":{},"source":["## Aggregation function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# How do we combine the values from multiple trajectores\n","def mean_std_of_ensemble(c, data):\n"," a = np.average(data)\n"," s = np.std(data)\n"," if c.verbose:\n"," print(f'mean_std_of_ensemble m:{a} s:{s}')\n"," return (a, s)"]},{"cell_type":"markdown","metadata":{},"source":["# Parameter Sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweep1D():\n","\n"," def run(c, kwargs, verbose=False):\n"," c.verbose = verbose\n"," fn = c.feature_extraction\n"," ag = c.ensemble_aggragator\n"," data = np.zeros((len(c.p1_range), 2)) # mean and std\n"," for i, v1 in enumerate(c.p1_range):\n"," if c.verbose:\n"," print(f'running {c.p1}={v1}')\n"," if(c.number_of_trajectories > 1):\n"," tmp_results = model.run(**kwargs, variables={c.p1:v1})\n"," (m, s) = ag([fn(x) for x in tmp_results])\n"," data[i, 0] = m\n"," data[i, 1] = s\n"," else:\n"," tmp_result = model.run(**kwargs, variables={c.p1:v1})\n"," data[i, 0] = c.feature_extraction(tmp_result)\n"," c.data = data\n","\n","\n"," def plot(c):\n"," from matplotlib import pyplot as plt\n"," from mpl_toolkits.axes_grid1 import make_axes_locatable\n"," fig, ax = plt.subplots(figsize=(8, 8))\n"," plt.title(f'Parameter Sweep - Variable:{c.variable_of_interest}')\n"," plt.errorbar(c.p1_range, c.data[:, 0], c.data[:, 1])\n"," plt.xlabel(c.p1, fontsize=16, fontweight='bold')\n"," plt.ylabel('Population', fontsize=16, fontweight='bold')\n","\n","\n"," def plotplotly(c, return_plotly_figure=False):\n"," from plotly.offline import iplot\n"," import plotly.graph_objs as go\n","\n"," visible = c.number_of_trajectories > 1\n"," error_y = dict(type='data', array=c.data[:, 1], visible=visible)\n","\n"," trace_list = [go.Scatter(x=c.p1_range, y=c.data[:, 0], error_y=error_y)]\n","\n"," title = dict(text=f'Parameter Sweep - Variable: {c.variable_of_interest}', x=0.5)\n"," yaxis_label = dict(title='Population')\n"," xaxis_label = dict(title=f'{c.p1}')\n","\n"," layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n","\n"," fig = dict(data=trace_list, layout=layout)\n","\n"," if return_plotly_figure:\n"," return fig\n"," iplot(fig)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweepConfig(ParameterSweep1D):\n"," # What class defines the GillesPy2 model\n"," model = ToggleSwitch()\n"," # ENTER PARAMETER HERE\n"," p1 = 'alpha1'\n"," # ENTER START VALUE FOR P1 RANGE HERE\n"," p1_min = 0.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER END VALUE FOR P1 RANGE HERE\n"," p1_max = 1.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER THE NUMBER OF STEPS FOR P1 HERE\n"," p1_steps = 11\n"," p1_range = np.linspace(p1_min, p1_max, p1_steps)\n"," # ENTER VARIABLE OF INTEREST HERE\n"," variable_of_interest = 'A'\n"," number_of_trajectories = 20\n"," # What feature of the simulation are we examining\n"," feature_extraction = population_at_last_timepoint\n"," # for ensemble resutls: how do we aggreggate the values\n"," ensemble_aggragator = mean_std_of_ensemble"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","ps = ParameterSweepConfig()\n","%time ps.run(kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plot()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plotplotly()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_2D_Parameter_Sweep.ipynb b/public_models/Toggle_Switch/Toggle_Switch_2D_Parameter_Sweep.ipynb deleted file mode 100644 index c5992dd1cf..0000000000 --- a/public_models/Toggle_Switch/Toggle_Switch_2D_Parameter_Sweep.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":20,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Post Processing"]},{"cell_type":"markdown","metadata":{},"source":["## Feature extraction function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# What value(s) do you want to extract from the simulation trajectory\n","def population_at_last_timepoint(c, res):\n"," if c.verbose:\n"," print(f'population_at_last_timepoint {c.variable_of_interest}={res[c.variable_of_interest][-1]}')\n"," return res[c.variable_of_interest][-1]"]},{"cell_type":"markdown","metadata":{},"source":["## Aggregation function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# How do we combine the values from multiple trajectores\n","def average_of_ensemble(c, data):\n"," a = np.average(data)\n"," if c.verbose:\n"," print(f'average_of_ensemble = {a}')\n"," return a"]},{"cell_type":"markdown","metadata":{},"source":["# Parameter Sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweep2D():\n","\n"," def run(c, kwargs, verbose=False):\n"," c.verbose = verbose\n"," fn = c.feature_extraction\n"," ag = c.ensemble_aggragator\n"," data = np.zeros((len(c.p1_range), len(c.p2_range)))\n"," for i, v1 in enumerate(c.p1_range):\n"," for j, v2 in enumerate(c.p2_range):\n"," if c.verbose:\n"," print(f'running {c.p1}={v1}, {c.p2}={v2}')\n"," if(c.number_of_trajectories > 1):\n"," tmp_results = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n"," data[i, j] = ag([fn(x) for x in tmp_results])\n"," else:\n"," tmp_result = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n"," data[i, j] = c.feature_extraction(tmp_result)\n"," c.data = data\n","\n","\n"," def plot(c):\n"," from matplotlib import pyplot as plt\n"," from mpl_toolkits.axes_grid1 import make_axes_locatable\n"," fig, ax = plt.subplots(figsize=(8, 8))\n"," plt.imshow(c.data)\n"," ax.set_xticks(np.arange(c.data.shape[1]) + 0.5, minor=False)\n"," ax.set_yticks(np.arange(c.data.shape[0]) + 0.5, minor=False)\n"," plt.title(f'Parameter Sweep - Variable: {c.variable_of_interest}')\n"," ax.set_xticklabels(c.p1_range, minor=False, rotation=90)\n"," ax.set_yticklabels(c.p2_range, minor=False)\n"," ax.set_xlabel(c.p1, fontsize=16, fontweight='bold')\n"," ax.set_ylabel(c.p2, fontsize=16, fontweight='bold')\n"," divider = make_axes_locatable(ax)\n"," cax = divider.append_axes('right', size='5%', pad=0.2)\n"," _ = plt.colorbar(ax=ax, cax=cax)\n","\n","\n"," def plotplotly(c, return_plotly_figure=False):\n"," from plotly.offline import init_notebook_mode, iplot\n"," import plotly.graph_objs as go\n","\n"," xaxis_ticks = c.p1_range\n"," yaxis_ticks = c.p2_range\n","\n"," trace_list = [go.Heatmap(z=c.data, x=xaxis_ticks, y=yaxis_ticks)]\n"," title = dict(text=f'Parameter Sweep - Variable: {c.variable_of_interest}', x=0.5)\n"," xaxis_label = dict(title=f'{c.p1}')\n"," yaxis_label = dict(title=f'{c.p2}')\n","\n"," layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n","\n"," fig = dict(data=trace_list, layout=layout)\n","\n"," if return_plotly_figure:\n"," return fig\n"," iplot(fig)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweepConfig(ParameterSweep2D):\n"," # What class defines the GillesPy2 model\n"," model = ToggleSwitch()\n"," # ENTER PARAMETER 1 HERE\n"," p1 = 'alpha1'\n"," # ENTER PARAMETER 2 HERE\n"," p2 = 'alpha2'\n"," # ENTER START VALUE FOR P1 RANGE HERE\n"," p1_min = 0.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER END VALUE FOR P1 RANGE HERE\n"," p1_max = 1.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER THE NUMBER OF STEPS FOR P1 HERE\n"," p1_steps = 11\n"," p1_range = np.linspace(p1_min, p1_max, p1_steps)\n"," # ENTER START VALUE FOR P2 RANGE HERE\n"," p2_min = 0.5 * float(eval(model.get_parameter(p2).expression))\n"," # ENTER END VALUE FOR P2 RANGE HERE\n"," p2_max = 1.5 * float(eval(model.get_parameter(p2).expression))\n"," # ENTER THE NUMBER OF STEPS FOR P2 HERE\n"," p2_steps = 11\n"," p2_range = np.linspace(p2_min, p2_max, p2_steps)\n"," # ENTER VARIABLE OF INTEREST HERE\n"," variable_of_interest = 'A'\n"," number_of_trajectories = 20\n"," # What feature of the simulation are we examining\n"," feature_extraction = population_at_last_timepoint\n"," # for ensemble resutls: how do we aggreggate the values\n"," ensemble_aggragator = average_of_ensemble"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","ps = ParameterSweepConfig()\n","%time ps.run(kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plot()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plotplotly()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Exploration.ipynb b/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Exploration.ipynb deleted file mode 100644 index 62f442c900..0000000000 --- a/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Exploration.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","%matplotlib notebook\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Exploration"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator function (using gillespy2 wrapper)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.utilities.gillespy2 import wrapper\n","settings = configure_simulation()\n","simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['A', 'B'])\n","expression_array = wrapper.get_parameter_expression_array(model)"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from dask.distributed import Client\n","\n","c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Define parameter sampler/design and summary statistics"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.designs import latin_hypercube_sampling\n","from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n","\n","lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n","lhc.generate_array(1000) # creates a LHD of size 1000\n","\n","# will use default minimal set of features\n","summary_stats = SummariesTSFRESH()"]},{"cell_type":"markdown","metadata":{},"source":["## Initiate StochMET"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.stochmet.stochmet import StochMET\n","\n","met = StochMET(simulator, lhc, summary_stats)"]},{"cell_type":"markdown","metadata":{},"source":["## Run parameter sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.compute(n_points=500, chunk_size=10)"]},{"cell_type":"markdown","metadata":{},"source":["## Explore the result"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First lets add some appropiate information about the model and features\n","met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n","met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n","met.data.configurations['listOfSummaries'] = met.summaries.features\n","met.data.configurations['timepoints'] = model.tspan"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Here we use UMAP for dimension reduction\n","met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.models.label_propagation import LPModel\n","# here lets use the dimension reduction embedding as input data\n","data = met.dr_model.embedding_\n","\n","model_lp = LPModel()\n","# train using basinhopping\n","model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# just to vislualize the result we will map the label distribution to the user_labels\n","# (will enable us to see the LP model output when using method 'explore')\n","user_labels = np.copy(met.data.user_labels)\n","# takes the label corresponding to index 0\n","met.data.user_labels = model_lp.model.label_distributions_[:, 0]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.data.user_labels = user_labels"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Inference.ipynb b/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Inference.ipynb deleted file mode 100644 index 90f880c7cf..0000000000 --- a/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Inference.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Inference"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2\n","\n","from tsfresh.feature_extraction.settings import MinimalFCParameters\n","from sciope.utilities.priors import uniform_prior\n","from sciope.utilities.summarystats import auto_tsfresh\n","from sciope.utilities.distancefunctions import naive_squared\n","from sciope.inference.abc_inference import ABC\n","from sklearn.metrics import mean_absolute_error\n","from dask.distributed import Client"]},{"cell_type":"markdown","metadata":{},"source":["## Generate some fixed(observed) data based on default parameters of the model"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","fixed_data = model.run(**kwargs)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Reshape the data and remove timepoints array\n","fixed_data = fixed_data.to_array()\n","fixed_data = np.asarray([x.T for x in fixed_data])\n","fixed_data = fixed_data[:, 1:, :]"]},{"cell_type":"markdown","metadata":{},"source":["## Define prior distribution"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# take default from mode 1 as reference\n","default_param = np.array(list(model.listOfParameters.items()))[:, 1]\n","\n","bound = []\n","for exp in default_param:\n"," bound.append(float(exp.expression))\n","\n","# Set the bounds\n","bound = np.array(bound)\n","dmin = bound * 0.1\n","dmax = bound * 2.0\n","\n","# Here we use uniform prior\n","uni_prior = uniform_prior.UniformPrior(dmin, dmax)"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def get_variables(params, model):\n"," # params - array, need to have the same order as model.listOfParameters\n"," variables = {}\n"," for e, pname in enumerate(model.listOfParameters.keys()):\n"," variables[pname] = params[e]\n"," return variables\n","\n","# Here we use the GillesPy2 Solver\n","def simulator(params, model):\n"," variables = get_variables(params, model)\n","\n"," res = model.run(**kwargs, variables=variables)\n"," res = res.to_array()\n"," tot_res = np.asarray([x.T for x in res]) # reshape to (N, S, T)\n"," # should not contain timepoints\n"," tot_res = tot_res[:, 1:, :]\n","\n"," return tot_res\n","\n","# Wrapper, simulator function to abc should should only take one argument (the parameter point)\n","def simulator2(x):\n"," return simulator(x, model=model)"]},{"cell_type":"markdown","metadata":{},"source":["## Define summary statistics and distance function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Function to generate summary statistics\n","summ_func = auto_tsfresh.SummariesTSFRESH()\n","\n","# Distance\n","ns = naive_squared.NaiveSquaredDistance()"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Start abc instance"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["abc = ABC(fixed_data, sim=simulator2, prior_function=uni_prior, summaries_function=summ_func.compute, distance_function=ns)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First compute the fixed(observed) mean\n","abc.compute_fixed_mean(chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Tyson_Oscillator/Tyson_Oscillator.sbml b/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.sbml similarity index 90% rename from public_models/Tyson_Oscillator/Tyson_Oscillator.sbml rename to public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.sbml index 9e791739e7..465896c1e8 100644 --- a/public_models/Tyson_Oscillator/Tyson_Oscillator.sbml +++ b/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.sbml @@ -1,12 +1,12 @@ - + - + - - + + diff --git a/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.ipynb b/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.ipynb new file mode 100644 index 0000000000..aa420d5431 --- /dev/null +++ b/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tyson 2 State Oscillator\n", + "***\n", + "Here, as a test case, we run a simple two-state oscillator (Novak & Tyson 2008) as an example of a stochastic reaction system.\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Tyson 2 State Oscillator Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_tyspn_2_state_oscillator(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Tyson_2_State_Oscillator\")\n", + " model.volume = 300\n", + "\n", + " # Variables\n", + " X = gillespy2.Species(name=\"X\", initial_value=196, mode=\"discrete\")\n", + " Y = gillespy2.Species(name=\"Y\", initial_value=255, mode=\"discrete\")\n", + " model.add_species([X, Y])\n", + "\n", + " # Parameters\n", + " P = gillespy2.Parameter(name=\"P\", expression=\"2\")\n", + " kt = gillespy2.Parameter(name=\"kt\", expression=\"20\")\n", + " kd = gillespy2.Parameter(name=\"kd\", expression=\"1\")\n", + " a0 = gillespy2.Parameter(name=\"a0\", expression=\"0.005\")\n", + " a1 = gillespy2.Parameter(name=\"a1\", expression=\"0.05\")\n", + " a2 = gillespy2.Parameter(name=\"a2\", expression=\"0.1\")\n", + " kdx = gillespy2.Parameter(name=\"kdx\", expression=\"1\")\n", + " model.add_parameter([P, kt, kd, a0, a1, a2, kdx])\n", + "\n", + " # Reactions\n", + " rxn1 = gillespy2.Reaction(\n", + " name=\"rxn1\",\n", + " reactants={}, products={'X': 1},\n", + " propensity_function=\"vol*1/(1+(Y*Y/((vol*vol))))\",\n", + " ode_propensity_function=\"vol*1/(1+(Y*Y/((vol*vol))))\"\n", + " )\n", + " rxn2 = gillespy2.Reaction(\n", + " name=\"rxn2\", rate=\"kdx\",\n", + " reactants={'X': 1}, products={}\n", + " )\n", + " rxn3 = gillespy2.Reaction(\n", + " name=\"rxn3\", rate=\"kt\",\n", + " reactants={'X': 1}, products={'X': 1, 'Y': 1}\n", + " )\n", + " rxn4 = gillespy2.Reaction(\n", + " name=\"rxn4\", rate=\"kd\",\n", + " reactants={'Y': 1}, products={}\n", + " )\n", + " rxn5 = gillespy2.Reaction(\n", + " name=\"rxn5\",\n", + " reactants={'Y': 1}, products={},\n", + " propensity_function=\"Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))\",\n", + " ode_propensity_function=\"Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))\"\n", + " )\n", + " model.add_reaction([rxn1, rxn2, rxn3, rxn4, rxn5])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, 20)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_tyspn_2_state_oscillator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.mdl b/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.mdl new file mode 100644 index 0000000000..0ff114cffa --- /dev/null +++ b/public_models/Tyson-Oscillator.proj/Tyson_2_State_Oscillator.wkgp/Tyson_2_State_Oscillator.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":15,"defaultMode":"discrete","annotation":"Here, as a test case, we run a simple two-state oscillator (Novak & Tyson 2008) as an example of a stochastic reaction system.","volume":300,"modelSettings":{"endSim":20,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"X","value":196,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"Y","value":255,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":3,"name":"P","expression":2,"annotation":""},{"compID":4,"name":"kt","expression":20,"annotation":""},{"compID":5,"name":"kd","expression":1,"annotation":""},{"compID":6,"name":"a0","expression":0.005,"annotation":""},{"compID":7,"name":"a1","expression":0.05,"annotation":""},{"compID":8,"name":"a2","expression":0.1,"annotation":""},{"compID":9,"name":"kdx","expression":1,"annotation":""}],"reactions":[{"compID":10,"name":"rxn1","massaction":false,"odePropensity":"vol*1/(1+(Y*Y/((vol*vol))))","propensity":"vol*1/(1+(Y*Y/((vol*vol))))","annotation":"","rate":{},"reactants":[],"products":[{"ratio":1,"specie":{"compID":1,"name":"X","value":196,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":11,"name":"rxn2","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":9,"name":"kdx","expression":1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"X","value":196,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[]},{"compID":12,"name":"rxn3","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":4,"name":"kt","expression":20,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":1,"name":"X","value":196,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":1,"name":"X","value":196,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":2,"name":"Y","value":255,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]},{"compID":13,"name":"rxn4","massaction":true,"odePropensity":"","propensity":"","annotation":"","rate":{"compID":5,"name":"kd","expression":1,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"Y","value":255,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[]},{"compID":14,"name":"rxn5","massaction":false,"odePropensity":"Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))","propensity":"Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))","annotation":"","rate":{},"reactants":[{"ratio":1,"specie":{"compID":2,"name":"Y","value":255,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb b/public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb deleted file mode 100644 index 67a0db11f3..0000000000 --- a/public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Tyson_Oscillator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class TysonOscillator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Tyson_Oscillator\")\n"," self.volume = 300\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"P\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"kt\", expression=\"20\"))\n"," self.add_parameter(Parameter(name=\"kd\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"a0\", expression=\"0.005\"))\n"," self.add_parameter(Parameter(name=\"a1\", expression=\"0.05\"))\n"," self.add_parameter(Parameter(name=\"a2\", expression=\"0.1\"))\n"," self.add_parameter(Parameter(name=\"kdx\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"X\", initial_value=197, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Y\", initial_value=255, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"rxn1\", reactants={}, products={'X': 1}, propensity_function=\"vol*1/(1+(Y*Y/((vol*vol))))\"))\n"," self.add_reaction(Reaction(name=\"rxn2\", reactants={'X': 1}, products={}, rate=self.listOfParameters[\"kdx\"]))\n"," self.add_reaction(Reaction(name=\"rxn3\", reactants={'X': 1}, products={'X': 1, 'Y': 1}, rate=self.listOfParameters[\"kt\"]))\n"," self.add_reaction(Reaction(name=\"rxn4\", reactants={'Y': 1}, products={}, rate=self.listOfParameters[\"kd\"]))\n"," self.add_reaction(Reaction(name=\"rxn5\", reactants={'Y': 1}, products={}, propensity_function=\"Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))\"))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = TysonOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Tyson_Oscillator/Tyson_Oscillator.mdl b/public_models/Tyson_Oscillator/Tyson_Oscillator.mdl deleted file mode 100644 index 70da488946..0000000000 --- a/public_models/Tyson_Oscillator/Tyson_Oscillator.mdl +++ /dev/null @@ -1,322 +0,0 @@ -{ - "is_spatial": false, - "defaultID": 15, - "defaultMode": "discrete", - "modelSettings": { - "endSim": 100, - "timeStep": 1, - "volume": 300 - }, - "meshSettings": { - "count": 2 - }, - "species": [ - { - "compID": 1, - "name": "X", - "value": 197, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - }, - { - "compID": 2, - "name": "Y", - "value": 255, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - ], - "initialConditions": [], - "parameters": [ - { - "compID": 3, - "name": "P", - "expression": "2", - "annotation": "" - }, - { - "compID": 4, - "name": "kt", - "expression": "20", - "annotation": "" - }, - { - "compID": 5, - "name": "kd", - "expression": "1", - "annotation": "" - }, - { - "compID": 6, - "name": "a0", - "expression": "0.005", - "annotation": "" - }, - { - "compID": 7, - "name": "a1", - "expression": "0.05", - "annotation": "" - }, - { - "compID": 8, - "name": "a2", - "expression": "0.1", - "annotation": "" - }, - { - "compID": 9, - "name": "kdx", - "expression": "1", - "annotation": "" - } - ], - "reactions": [ - { - "compID": 10, - "name": "rxn1", - "reactionType": "custom-propensity", - "summary": "\\emptyset \\rightarrow X", - "massaction": false, - "propensity": "vol*1/(1+(Y*Y/((vol*vol))))", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "X", - "value": 197, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 11, - "name": "rxn2", - "reactionType": "destruction", - "summary": "X \\rightarrow \\emptyset", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 9, - "name": "kdx", - "expression": "1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "X", - "value": 197, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [] - }, - { - "compID": 12, - "name": "rxn3", - "reactionType": "split", - "summary": "X \\rightarrow X+Y", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 4, - "name": "kt", - "expression": "20", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "X", - "value": 197, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [ - { - "ratio": 1, - "specie": { - "compID": 1, - "name": "X", - "value": 197, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - }, - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Y", - "value": 255, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ] - }, - { - "compID": 13, - "name": "rxn4", - "reactionType": "destruction", - "summary": "Y \\rightarrow \\emptyset", - "massaction": true, - "propensity": "", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": { - "compID": 5, - "name": "kd", - "expression": "1", - "annotation": "" - }, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Y", - "value": 255, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [] - }, - { - "compID": 14, - "name": "rxn5", - "reactionType": "custom-propensity", - "summary": "Y \\rightarrow \\emptyset", - "massaction": false, - "propensity": "Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))", - "annotation": "", - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ], - "rate": {}, - "reactants": [ - { - "ratio": 1, - "specie": { - "compID": 2, - "name": "Y", - "value": 255, - "mode": "discrete", - "switchTol": 0.03, - "switchMin": 100, - "isSwitchTol": true, - "annotation": "", - "diffusionCoeff": 0, - "subdomains": [ - "subdomain 1: ", - "subdomain 2: " - ] - } - } - ], - "products": [] - } - ], - "rules": [], - "eventsCollection": [], - "functionDefinitions": [] -} \ No newline at end of file diff --git a/public_models/Vilar_Oscillator/Vilar_Oscillator.sbml b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.sbml similarity index 100% rename from public_models/Vilar_Oscillator/Vilar_Oscillator.sbml rename to public_models/Vilar-Oscillator.proj/Vilar_Oscillator.sbml diff --git a/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.ipynb b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.ipynb new file mode 100644 index 0000000000..9aa08b429b --- /dev/null +++ b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.ipynb @@ -0,0 +1,250 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vilar Oscillator\n", + "***\n", + "**From:** Vilar, José M. G. et al. “Mechanisms of noise-resistance in genetic oscillators.” PNAS, vol. 99 no. 9, 2002, pp. 5988-5992., doi.org/10.1073/pnas.092133899.\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Vilar Oscillator Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_vilar_oscillator(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Vilar_Oscillator\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " Da = gillespy2.Species(name=\"Da\", initial_value=1, mode=\"discrete\")\n", + " Da_prime = gillespy2.Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\")\n", + " Ma = gillespy2.Species(name=\"Ma\", initial_value=0, mode=\"discrete\")\n", + " Dr = gillespy2.Species(name=\"Dr\", initial_value=1, mode=\"discrete\")\n", + " Dr_prime = gillespy2.Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\")\n", + " Mr = gillespy2.Species(name=\"Mr\", initial_value=0, mode=\"discrete\")\n", + " C = gillespy2.Species(name=\"C\", initial_value=0, mode=\"discrete\")\n", + " A = gillespy2.Species(name=\"A\", initial_value=0, mode=\"discrete\")\n", + " R = gillespy2.Species(name=\"R\", initial_value=0, mode=\"discrete\")\n", + " model.add_species([Da, Da_prime, Ma, Dr, Dr_prime, Mr, C, A, R])\n", + "\n", + " # Parameters\n", + " alphaA = gillespy2.Parameter(name=\"alphaA\", expression=\"50\")\n", + " alphaA_prime = gillespy2.Parameter(name=\"alphaA_prime\", expression=\"500\")\n", + " alphaR = gillespy2.Parameter(name=\"alphaR\", expression=\"0.01\")\n", + " alphaR_prime = gillespy2.Parameter(name=\"alphaR_prime\", expression=\"50\")\n", + " betaA = gillespy2.Parameter(name=\"betaA\", expression=\"50\")\n", + " betaR = gillespy2.Parameter(name=\"betaR\", expression=\"5\")\n", + " deltaMA = gillespy2.Parameter(name=\"deltaMA\", expression=\"10\")\n", + " deltaMR = gillespy2.Parameter(name=\"deltaMR\", expression=\"0.5\")\n", + " deltaA = gillespy2.Parameter(name=\"deltaA\", expression=\"1\")\n", + " deltaR = gillespy2.Parameter(name=\"deltaR\", expression=\"0.2\")\n", + " gammaA = gillespy2.Parameter(name=\"gammaA\", expression=\"1\")\n", + " gammaR = gillespy2.Parameter(name=\"gammaR\", expression=\"1\")\n", + " gammaC = gillespy2.Parameter(name=\"gammaC\", expression=\"2\")\n", + " thetaA = gillespy2.Parameter(name=\"thetaA\", expression=\"50\")\n", + " thetaR = gillespy2.Parameter(name=\"thetaR\", expression=\"100\")\n", + " model.add_parameter([\n", + " alphaA, alphaA_prime, alphaR, alphaR_prime, betaA, betaR, deltaMA,\n", + " deltaMR, deltaA, deltaR, gammaA, gammaR, gammaC, thetaA, thetaR\n", + " ])\n", + "\n", + " # Reactions\n", + " r1 = gillespy2.Reaction(\n", + " name=\"r1\", rate=\"gammaC\",\n", + " reactants={'A': 1, 'R': 1}, products={'C': 1}\n", + " )\n", + " r2 = gillespy2.Reaction(\n", + " name=\"r2\", rate=\"deltaA\",\n", + " reactants={'A': 1}, products={}\n", + " )\n", + " r3 = gillespy2.Reaction(\n", + " name=\"r3\", rate=\"deltaA\",\n", + " reactants={'C': 1}, products={'R': 1}\n", + " )\n", + " r4 = gillespy2.Reaction(\n", + " name=\"r4\", rate=\"deltaR\",\n", + " reactants={'R': 1}, products={}\n", + " )\n", + " r5 = gillespy2.Reaction(\n", + " name=\"r5\", rate=\"gammaA\",\n", + " reactants={'A': 1, 'Da': 1}, products={'Da_prime': 1}\n", + " )\n", + " r6 = gillespy2.Reaction(\n", + " name=\"r6\", rate=\"thetaA\",\n", + " reactants={'Da_prime': 1}, products={'A': 1, 'Da': 1}\n", + " )\n", + " r7 = gillespy2.Reaction(\n", + " name=\"r7\", rate=\"alphaA\",\n", + " reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}\n", + " )\n", + " r8 = gillespy2.Reaction(\n", + " name=\"r8\", rate=\"alphaA_prime\",\n", + " reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}\n", + " )\n", + " r9 = gillespy2.Reaction(\n", + " name=\"r9\", rate=\"deltaMA\",\n", + " reactants={'Ma': 1}, products={}\n", + " )\n", + " r10 = gillespy2.Reaction(\n", + " name=\"r10\", rate=\"betaA\",\n", + " reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}\n", + " )\n", + " r11 = gillespy2.Reaction(\n", + " name=\"r11\", rate=\"gammaR\",\n", + " reactants={'A': 1, 'Dr': 1}, products={'Dr_prime': 1}\n", + " )\n", + " r12 = gillespy2.Reaction(\n", + " name=\"r12\", rate=\"thetaR\",\n", + " reactants={'Dr_prime': 1}, products={'A': 1, 'Dr': 1}\n", + " )\n", + " r13 = gillespy2.Reaction(\n", + " name=\"r13\", rate=\"alphaR\",\n", + " reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}\n", + " )\n", + " r14 = gillespy2.Reaction(\n", + " name=\"r14\", rate=\"alphaR_prime\",\n", + " reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}\n", + " )\n", + " r15 = gillespy2.Reaction(\n", + " name=\"r15\", rate=\"deltaMR\",\n", + " reactants={'Mr': 1}, products={}\n", + " )\n", + " r16 = gillespy2.Reaction(\n", + " name=\"r16\", rate=\"betaR\",\n", + " reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}\n", + " )\n", + " model.add_reaction([r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=200)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_vilar_oscillator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"algorithm\":\"SSA\",\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plotplotly()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.mdl b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.mdl new file mode 100644 index 0000000000..eb43b7a0aa --- /dev/null +++ b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator.mdl @@ -0,0 +1 @@ +{"is_spatial":false,"defaultID":61,"defaultMode":"discrete","annotation":"**From:** Vilar, José M. 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\ No newline at end of file diff --git a/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Exploration.ipynb b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Exploration.ipynb new file mode 100644 index 0000000000..72d8139350 --- /dev/null +++ b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Exploration.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vilar Oscillator\n", + "***\n", + "**From:** Vilar, José M. G. et al. “Mechanisms of noise-resistance in genetic oscillators.” PNAS, vol. 99 no. 9, 2002, pp. 5988-5992., doi.org/10.1073/pnas.092133899.\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "from dask.distributed import Client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sciope.utilities.gillespy2 import wrapper\n", + "from sciope.designs import latin_hypercube_sampling\n", + "from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n", + "from sciope.stochmet.stochmet import StochMET\n", + "from sciope.models.label_propagation import LPModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Vilar Oscillator Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_vilar_oscillator(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Vilar_Oscillator\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " Da = gillespy2.Species(name=\"Da\", initial_value=1, mode=\"discrete\")\n", + " Da_prime = gillespy2.Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\")\n", + " Ma = gillespy2.Species(name=\"Ma\", initial_value=0, mode=\"discrete\")\n", + " Dr = gillespy2.Species(name=\"Dr\", initial_value=1, mode=\"discrete\")\n", + " Dr_prime = gillespy2.Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\")\n", + " Mr = gillespy2.Species(name=\"Mr\", initial_value=0, mode=\"discrete\")\n", + " C = gillespy2.Species(name=\"C\", initial_value=0, mode=\"discrete\")\n", + " A = gillespy2.Species(name=\"A\", initial_value=0, mode=\"discrete\")\n", + " R = gillespy2.Species(name=\"R\", initial_value=0, mode=\"discrete\")\n", + " model.add_species([Da, Da_prime, Ma, Dr, Dr_prime, Mr, C, A, R])\n", + "\n", + " # Parameters\n", + " alphaA = gillespy2.Parameter(name=\"alphaA\", expression=\"50\")\n", + " alphaA_prime = gillespy2.Parameter(name=\"alphaA_prime\", expression=\"500\")\n", + " alphaR = gillespy2.Parameter(name=\"alphaR\", expression=\"0.01\")\n", + " alphaR_prime = gillespy2.Parameter(name=\"alphaR_prime\", expression=\"50\")\n", + " betaA = gillespy2.Parameter(name=\"betaA\", expression=\"50\")\n", + " betaR = gillespy2.Parameter(name=\"betaR\", expression=\"5\")\n", + " deltaMA = gillespy2.Parameter(name=\"deltaMA\", expression=\"10\")\n", + " deltaMR = gillespy2.Parameter(name=\"deltaMR\", expression=\"0.5\")\n", + " deltaA = gillespy2.Parameter(name=\"deltaA\", expression=\"1\")\n", + " deltaR = gillespy2.Parameter(name=\"deltaR\", expression=\"0.2\")\n", + " gammaA = gillespy2.Parameter(name=\"gammaA\", expression=\"1\")\n", + " gammaR = gillespy2.Parameter(name=\"gammaR\", expression=\"1\")\n", + " gammaC = gillespy2.Parameter(name=\"gammaC\", expression=\"2\")\n", + " thetaA = gillespy2.Parameter(name=\"thetaA\", expression=\"50\")\n", + " thetaR = gillespy2.Parameter(name=\"thetaR\", expression=\"100\")\n", + " model.add_parameter([\n", + " alphaA, alphaA_prime, alphaR, alphaR_prime, betaA, betaR, deltaMA,\n", + " deltaMR, deltaA, deltaR, gammaA, gammaR, gammaC, thetaA, thetaR\n", + " ])\n", + "\n", + " # Reactions\n", + " r1 = gillespy2.Reaction(\n", + " name=\"r1\", rate=\"gammaC\",\n", + " reactants={'A': 1, 'R': 1}, products={'C': 1}\n", + " )\n", + " r2 = gillespy2.Reaction(\n", + " name=\"r2\", rate=\"deltaA\",\n", + " reactants={'A': 1}, products={}\n", + " )\n", + " r3 = gillespy2.Reaction(\n", + " name=\"r3\", rate=\"deltaA\",\n", + " reactants={'C': 1}, products={'R': 1}\n", + " )\n", + " r4 = gillespy2.Reaction(\n", + " name=\"r4\", rate=\"deltaR\",\n", + " reactants={'R': 1}, products={}\n", + " )\n", + " r5 = gillespy2.Reaction(\n", + " name=\"r5\", rate=\"gammaA\",\n", + " reactants={'A': 1, 'Da': 1}, products={'Da_prime': 1}\n", + " )\n", + " r6 = gillespy2.Reaction(\n", + " name=\"r6\", rate=\"thetaA\",\n", + " reactants={'Da_prime': 1}, products={'A': 1, 'Da': 1}\n", + " )\n", + " r7 = gillespy2.Reaction(\n", + " name=\"r7\", rate=\"alphaA\",\n", + " reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}\n", + " )\n", + " r8 = gillespy2.Reaction(\n", + " name=\"r8\", rate=\"alphaA_prime\",\n", + " reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}\n", + " )\n", + " r9 = gillespy2.Reaction(\n", + " name=\"r9\", rate=\"deltaMA\",\n", + " reactants={'Ma': 1}, products={}\n", + " )\n", + " r10 = gillespy2.Reaction(\n", + " name=\"r10\", rate=\"betaA\",\n", + " reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}\n", + " )\n", + " r11 = gillespy2.Reaction(\n", + " name=\"r11\", rate=\"gammaR\",\n", + " reactants={'A': 1, 'Dr': 1}, products={'Dr_prime': 1}\n", + " )\n", + " r12 = gillespy2.Reaction(\n", + " name=\"r12\", rate=\"thetaR\",\n", + " reactants={'Dr_prime': 1}, products={'A': 1, 'Dr': 1}\n", + " )\n", + " r13 = gillespy2.Reaction(\n", + " name=\"r13\", rate=\"alphaR\",\n", + " reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}\n", + " )\n", + " r14 = gillespy2.Reaction(\n", + " name=\"r14\", rate=\"alphaR_prime\",\n", + " reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}\n", + " )\n", + " r15 = gillespy2.Reaction(\n", + " name=\"r15\", rate=\"deltaMR\",\n", + " reactants={'Mr': 1}, products={}\n", + " )\n", + " r16 = gillespy2.Reaction(\n", + " name=\"r16\", rate=\"betaR\",\n", + " reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}\n", + " )\n", + " model.add_reaction([r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=200)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_vilar_oscillator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " solver = gillespy2.SSACSolver(model=model)\n", + " kwargs = {\n", + " \"solver\":solver,\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Model Exploration\n", + "***\n", + "### Define simulator function (using gillespy2 wrapper)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "settings = configure_simulation()\n", + "simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['Da', 'Da_prime', 'Ma', 'Dr', 'Dr_prime', 'Mr', 'C', 'A', 'R'])\n", + "expression_array = wrapper.get_parameter_expression_array(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start local cluster using dask client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = Client()\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define parameter sampler/design and summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n", + "lhc.generate_array(1000) # creates a LHD of size 1000\n", + "\n", + "# will use default minimal set of features\n", + "summary_stats = SummariesTSFRESH()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initiate StochMET" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met = StochMET(simulator, lhc, summary_stats)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run parameter sweep\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.compute(n_points=500, chunk_size=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Explore the result\n", + "***\n", + "First lets add some appropiate information about the model and features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n", + "met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n", + "met.data.configurations['listOfSummaries'] = met.summaries.features\n", + "met.data.configurations['timepoints'] = model.tspan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use UMAP for dimension reduction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.explore(dr_method='umap')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here lets use the dimension reduction embedding as input data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = met.dr_model.embedding_\n", + "\n", + "model_lp = LPModel()\n", + "# train using basinhopping\n", + "model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just to vislualize the result we will map the label distribution to the user_labels (will enable us to see the LP model output when using method 'explore')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "user_labels = numpy.copy(met.data.user_labels)\n", + "# takes the label corresponding to index 0\n", + "met.data.user_labels = model_lp.model.label_distributions_[:, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.explore(dr_method='umap')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "met.data.user_labels = user_labels" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Inference.ipynb b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Inference.ipynb new file mode 100644 index 0000000000..66cf194f3f --- /dev/null +++ b/public_models/Vilar-Oscillator.proj/Vilar_Oscillator.wkgp/Vilar_Oscillator_SCIOPE_Model_Inference.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vilar Oscillator\n", + "***\n", + "**From:** Vilar, José M. G. et al. “Mechanisms of noise-resistance in genetic oscillators.” PNAS, vol. 99 no. 9, 2002, pp. 5988-5992., doi.org/10.1073/pnas.092133899.\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "from tsfresh.feature_extraction.settings import MinimalFCParameters\n", + "from dask.distributed import Client\n", + "from sklearn.metrics import mean_absolute_error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gillespy2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sciope.utilities.priors import uniform_prior\n", + "from sciope.utilities.summarystats import auto_tsfresh\n", + "from sciope.utilities.distancefunctions import naive_squared\n", + "from sciope.inference.abc_inference import ABC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Vilar Oscillator Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_vilar_oscillator(parameter_values=None):\n", + " model = gillespy2.Model(name=\"Vilar_Oscillator\")\n", + " model.volume = 1\n", + "\n", + " # Variables\n", + " Da = gillespy2.Species(name=\"Da\", initial_value=1, mode=\"discrete\")\n", + " Da_prime = gillespy2.Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\")\n", + " Ma = gillespy2.Species(name=\"Ma\", initial_value=0, mode=\"discrete\")\n", + " Dr = gillespy2.Species(name=\"Dr\", initial_value=1, mode=\"discrete\")\n", + " Dr_prime = gillespy2.Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\")\n", + " Mr = gillespy2.Species(name=\"Mr\", initial_value=0, mode=\"discrete\")\n", + " C = gillespy2.Species(name=\"C\", initial_value=0, mode=\"discrete\")\n", + " A = gillespy2.Species(name=\"A\", initial_value=0, mode=\"discrete\")\n", + " R = gillespy2.Species(name=\"R\", initial_value=0, mode=\"discrete\")\n", + " model.add_species([Da, Da_prime, Ma, Dr, Dr_prime, Mr, C, A, R])\n", + "\n", + " # Parameters\n", + " alphaA = gillespy2.Parameter(name=\"alphaA\", expression=\"50\")\n", + " alphaA_prime = gillespy2.Parameter(name=\"alphaA_prime\", expression=\"500\")\n", + " alphaR = gillespy2.Parameter(name=\"alphaR\", expression=\"0.01\")\n", + " alphaR_prime = gillespy2.Parameter(name=\"alphaR_prime\", expression=\"50\")\n", + " betaA = gillespy2.Parameter(name=\"betaA\", expression=\"50\")\n", + " betaR = gillespy2.Parameter(name=\"betaR\", expression=\"5\")\n", + " deltaMA = gillespy2.Parameter(name=\"deltaMA\", expression=\"10\")\n", + " deltaMR = gillespy2.Parameter(name=\"deltaMR\", expression=\"0.5\")\n", + " deltaA = gillespy2.Parameter(name=\"deltaA\", expression=\"1\")\n", + " deltaR = gillespy2.Parameter(name=\"deltaR\", expression=\"0.2\")\n", + " gammaA = gillespy2.Parameter(name=\"gammaA\", expression=\"1\")\n", + " gammaR = gillespy2.Parameter(name=\"gammaR\", expression=\"1\")\n", + " gammaC = gillespy2.Parameter(name=\"gammaC\", expression=\"2\")\n", + " thetaA = gillespy2.Parameter(name=\"thetaA\", expression=\"50\")\n", + " thetaR = gillespy2.Parameter(name=\"thetaR\", expression=\"100\")\n", + " model.add_parameter([\n", + " alphaA, alphaA_prime, alphaR, alphaR_prime, betaA, betaR, deltaMA,\n", + " deltaMR, deltaA, deltaR, gammaA, gammaR, gammaC, thetaA, thetaR\n", + " ])\n", + "\n", + " # Reactions\n", + " r1 = gillespy2.Reaction(\n", + " name=\"r1\", rate=\"gammaC\",\n", + " reactants={'A': 1, 'R': 1}, products={'C': 1}\n", + " )\n", + " r2 = gillespy2.Reaction(\n", + " name=\"r2\", rate=\"deltaA\",\n", + " reactants={'A': 1}, products={}\n", + " )\n", + " r3 = gillespy2.Reaction(\n", + " name=\"r3\", rate=\"deltaA\",\n", + " reactants={'C': 1}, products={'R': 1}\n", + " )\n", + " r4 = gillespy2.Reaction(\n", + " name=\"r4\", rate=\"deltaR\",\n", + " reactants={'R': 1}, products={}\n", + " )\n", + " r5 = gillespy2.Reaction(\n", + " name=\"r5\", rate=\"gammaA\",\n", + " reactants={'A': 1, 'Da': 1}, products={'Da_prime': 1}\n", + " )\n", + " r6 = gillespy2.Reaction(\n", + " name=\"r6\", rate=\"thetaA\",\n", + " reactants={'Da_prime': 1}, products={'A': 1, 'Da': 1}\n", + " )\n", + " r7 = gillespy2.Reaction(\n", + " name=\"r7\", rate=\"alphaA\",\n", + " reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}\n", + " )\n", + " r8 = gillespy2.Reaction(\n", + " name=\"r8\", rate=\"alphaA_prime\",\n", + " reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}\n", + " )\n", + " r9 = gillespy2.Reaction(\n", + " name=\"r9\", rate=\"deltaMA\",\n", + " reactants={'Ma': 1}, products={}\n", + " )\n", + " r10 = gillespy2.Reaction(\n", + " name=\"r10\", rate=\"betaA\",\n", + " reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}\n", + " )\n", + " r11 = gillespy2.Reaction(\n", + " name=\"r11\", rate=\"gammaR\",\n", + " reactants={'A': 1, 'Dr': 1}, products={'Dr_prime': 1}\n", + " )\n", + " r12 = gillespy2.Reaction(\n", + " name=\"r12\", rate=\"thetaR\",\n", + " reactants={'Dr_prime': 1}, products={'A': 1, 'Dr': 1}\n", + " )\n", + " r13 = gillespy2.Reaction(\n", + " name=\"r13\", rate=\"alphaR\",\n", + " reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}\n", + " )\n", + " r14 = gillespy2.Reaction(\n", + " name=\"r14\", rate=\"alphaR_prime\",\n", + " reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}\n", + " )\n", + " r15 = gillespy2.Reaction(\n", + " name=\"r15\", rate=\"deltaMR\",\n", + " reactants={'Mr': 1}, products={}\n", + " )\n", + " r16 = gillespy2.Reaction(\n", + " name=\"r16\", rate=\"betaR\",\n", + " reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}\n", + " )\n", + " model.add_reaction([r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16])\n", + "\n", + " # Timespan\n", + " tspan = gillespy2.TimeSpan.arange(1, t=200)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = VilarOscillator1()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " solver = gillespy2.SSACSolver(model=model)\n", + " kwargs = {\n", + " \"solver\":solver,\n", + " \"number_of_trajectories\":100,\n", + " # \"seed\":None,\n", + " # \"tau_tol\":0.03,\n", + " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Model Inference\n", + "***\n", + "### Generate some fixed(observed) data based on default parameters of the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "fixed_data = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reshape the data and remove timepoints array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fixed_data = fixed_data.to_array()\n", + "fixed_data = numpy.asarray([x.T for x in fixed_data])\n", + "fixed_data = fixed_data[:, 1:, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define prior distribution\n", + "Take default from mode 1 as reference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "default_param = numpy.array(list(model.listOfParameters.items()))[:, 1]\n", + "\n", + "bound = []\n", + "for exp in default_param:\n", + " bound.append(float(exp.expression))\n", + "\n", + "# Set the bounds\n", + "bound = numpy.array(bound)\n", + "dmin = bound * 0.1\n", + "dmax = bound * 2.0\n", + "\n", + "# Here we use uniform prior\n", + "uni_prior = uniform_prior.UniformPrior(dmin, dmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define simulator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_variables(params, model):\n", + " # params - array, need to have the same order as model.listOfParameters\n", + " variables = {}\n", + " for e, pname in enumerate(model.listOfParameters.keys()):\n", + " variables[pname] = params[e]\n", + " return variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we use the GillesPy2 Solver" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def simulator(params, model):\n", + " variables = get_variables(params, model)\n", + "\n", + " res = model.run(**kwargs, variables=variables)\n", + " res = res.to_array()\n", + " tot_res = numpy.asarray([x.T for x in res]) # reshape to (N, S, T)\n", + " # should not contain timepoints\n", + " tot_res = tot_res[:, 1:, :]\n", + "\n", + " return tot_res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wrapper, simulator function to abc should should only take one argument (the parameter point)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def simulator2(x):\n", + " return simulator(x, model=model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define summary statistics and distance function\n", + "Function to generate summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summ_func = auto_tsfresh.SummariesTSFRESH()\n", + "\n", + "# Distance\n", + "ns = naive_squared.NaiveSquaredDistance()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Start local cluster using dask client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = Client()\n", + "c" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the abc instance\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abc = ABC(\n", + " fixed_data, sim=simulator2, prior_function=uni_prior,\n", + " summaries_function=summ_func.compute, distance_function=ns\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First compute the fixed(observed) mean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "abc.compute_fixed_mean(chunk_size=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb b/public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb deleted file mode 100644 index b02db31463..0000000000 --- a/public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb +++ /dev/null @@ -1,152 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n", - " EventTrigger, EventAssignment, RateRule, \\\n", - " AssignmentRule, FunctionDefinition\n", - "# from gillespy2 import SSACSolver\n", - "# from gillespy2 import TauLeapingCSolver\n", - "# from gillespy2 import ODECSolver\n", - "# from gillespy2 import TauHybridSolver" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vilar_Oscillator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class VilarOscillator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Vilar_Oscillator\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alphaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"alphaA_prime\", expression=\"500\"))\n", - " self.add_parameter(Parameter(name=\"alphaR\", expression=\"0.01\"))\n", - " self.add_parameter(Parameter(name=\"alphaR_prime\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"betaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"betaR\", expression=\"5\"))\n", - " self.add_parameter(Parameter(name=\"deltaMA\", expression=\"10\"))\n", - " self.add_parameter(Parameter(name=\"deltaMR\", expression=\"0.5\"))\n", - " self.add_parameter(Parameter(name=\"deltaA\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"deltaR\", expression=\"0.2\"))\n", - " self.add_parameter(Parameter(name=\"gammaA\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"gammaR\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"gammaC\", expression=\"2\"))\n", - " self.add_parameter(Parameter(name=\"thetaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"thetaR\", expression=\"100\"))\n", - "\n", - " # Variables\n", - " self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"C\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"A\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"R\", initial_value=0, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r1\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=\"gammaC\"))\n", - " self.add_reaction(Reaction(name=\"r2\", reactants={'A': 1}, products={}, rate=\"deltaA\"))\n", - " self.add_reaction(Reaction(name=\"r3\", reactants={'C': 1}, products={'R': 1}, rate=\"deltaA\"))\n", - " self.add_reaction(Reaction(name=\"r4\", reactants={'R': 1}, products={}, rate=\"deltaR\"))\n", - " self.add_reaction(Reaction(name=\"r5\", reactants={'A': 1, 'Da': 1}, products={'Da_prime': 1}, rate=\"gammaA\"))\n", - " self.add_reaction(Reaction(name=\"r6\", reactants={'Da_prime': 1}, products={'A': 1, 'Da': 1}, rate=\"thetaA\"))\n", - " self.add_reaction(Reaction(name=\"r7\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=\"alphaA\"))\n", - " self.add_reaction(Reaction(name=\"r8\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=\"alphaA_prime\"))\n", - " self.add_reaction(Reaction(name=\"r9\", reactants={'Ma': 1}, products={}, rate=\"deltaMA\"))\n", - " self.add_reaction(Reaction(name=\"r10\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=\"betaA\"))\n", - " self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1, 'Dr': 1}, products={'Dr_prime': 1}, rate=\"gammaR\"))\n", - " self.add_reaction(Reaction(name=\"r12\", reactants={'Dr_prime': 1}, products={'A': 1, 'Dr': 1}, rate=\"thetaR\"))\n", - " self.add_reaction(Reaction(name=\"r13\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=\"alphaR\"))\n", - " self.add_reaction(Reaction(name=\"r14\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=\"alphaR_prime\"))\n", - " self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=\"deltaMR\"))\n", - " self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=\"betaR\"))\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 201, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = VilarOscillator()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " # solver = SSACSolver(model=model)\n", - " kwargs = {\n", - " # \"solver\":solver,\n", - " \"number_of_trajectories\":100,\n", - " # \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.plotplotly()" - ] - } - ], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/public_models/Vilar_Oscillator/Vilar_Oscillator.mdl b/public_models/Vilar_Oscillator/Vilar_Oscillator.mdl deleted file mode 100644 index 79020fb762..0000000000 --- a/public_models/Vilar_Oscillator/Vilar_Oscillator.mdl +++ /dev/null @@ -1 +0,0 @@ -{"is_spatial":false,"defaultID":61,"defaultMode":"discrete","annotation":"","volume":1,"modelSettings":{"endSim":200,"timeStep":1},"domain":{"types":[],"particles":[]},"species":[{"compID":1,"name":"Da","value":1,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":2,"name":"Da_prime","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":3,"name":"Ma","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":4,"name":"Dr","value":1,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":5,"name":"Dr_prime","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":6,"name":"Mr","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":7,"name":"C","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":8,"name":"A","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""},{"compID":9,"name":"R","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}],"initialConditions":[],"parameters":[{"compID":10,"name":"alphaA","expression":50,"annotation":""},{"compID":11,"name":"alphaA_prime","expression":500,"annotation":""},{"compID":12,"name":"alphaR","expression":0.01,"annotation":""},{"compID":13,"name":"alphaR_prime","expression":50,"annotation":""},{"compID":14,"name":"betaA","expression":50,"annotation":""},{"compID":15,"name":"betaR","expression":5,"annotation":""},{"compID":16,"name":"deltaMA","expression":10,"annotation":""},{"compID":17,"name":"deltaMR","expression":0.5,"annotation":""},{"compID":18,"name":"deltaA","expression":1,"annotation":""},{"compID":19,"name":"deltaR","expression":0.2,"annotation":""},{"compID":20,"name":"gammaA","expression":1,"annotation":""},{"compID":21,"name":"gammaR","expression":1,"annotation":""},{"compID":22,"name":"gammaC","expression":2,"annotation":""},{"compID":23,"name":"thetaA","expression":50,"annotation":""},{"compID":24,"name":"thetaR","expression":100,"annotation":""}],"reactions":[{"compID":45,"name":"r1","reactionType":"merge","summary":"A+R 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Mr+R","massaction":true,"propensity":"","annotation":"","types":[],"rate":{"compID":15,"name":"betaR","expression":5,"annotation":""},"reactants":[{"ratio":1,"specie":{"compID":6,"name":"Mr","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}],"products":[{"ratio":1,"specie":{"compID":6,"name":"Mr","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}},{"ratio":1,"specie":{"compID":9,"name":"R","value":0,"mode":"discrete","switchTol":0.03,"switchMin":100,"isSwitchTol":true,"annotation":""}}]}],"rules":[],"eventsCollection":[],"functionDefinitions":[],"boundaryConditions":[]} \ No newline at end of file diff --git a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelExploration.ipynb b/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelExploration.ipynb deleted file mode 100644 index f01589b496..0000000000 --- a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelExploration.ipynb +++ /dev/null @@ -1,330 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n", - " EventTrigger, EventAssignment, RateRule, \\\n", - " AssignmentRule, FunctionDefinition\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import TauLeapingCSolver\n", - "# from gillespy2 import ODECSolver\n", - "# from gillespy2 import TauHybridSolver" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vilar_Oscillator" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "class VilarOscillator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Vilar_Oscillator\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alphaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"alphaA_prime\", expression=\"500\"))\n", - " self.add_parameter(Parameter(name=\"alphaR\", expression=\"0.01\"))\n", - " self.add_parameter(Parameter(name=\"alphaR_prime\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"betaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"betaR\", expression=\"5\"))\n", - " self.add_parameter(Parameter(name=\"deltaMA\", expression=\"10\"))\n", - " self.add_parameter(Parameter(name=\"deltaMR\", expression=\"0.5\"))\n", - " self.add_parameter(Parameter(name=\"deltaA\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"deltaR\", expression=\"0.2\"))\n", - " self.add_parameter(Parameter(name=\"gammaA\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"gammaR\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"gammaC\", expression=\"2\"))\n", - " self.add_parameter(Parameter(name=\"thetaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"thetaR\", expression=\"100\"))\n", - "\n", - " # Variables\n", - " self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"C\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"A\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"R\", initial_value=0, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r1\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=\"gammaC\"))\n", - " self.add_reaction(Reaction(name=\"r2\", reactants={'A': 1}, products={}, rate=\"deltaA\"))\n", - " self.add_reaction(Reaction(name=\"r3\", reactants={'C': 1}, products={'R': 1}, rate=\"deltaA\"))\n", - " self.add_reaction(Reaction(name=\"r4\", reactants={'R': 1}, products={}, rate=\"deltaR\"))\n", - " self.add_reaction(Reaction(name=\"r5\", reactants={'A': 1, 'Da': 1}, products={'Da_prime': 1}, rate=\"gammaA\"))\n", - " self.add_reaction(Reaction(name=\"r6\", reactants={'Da_prime': 1}, products={'A': 1, 'Da': 1}, rate=\"thetaA\"))\n", - " self.add_reaction(Reaction(name=\"r7\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=\"alphaA\"))\n", - " self.add_reaction(Reaction(name=\"r8\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=\"alphaA_prime\"))\n", - " self.add_reaction(Reaction(name=\"r9\", reactants={'Ma': 1}, products={}, rate=\"deltaMA\"))\n", - " self.add_reaction(Reaction(name=\"r10\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=\"betaA\"))\n", - " self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1, 'Dr': 1}, products={'Dr_prime': 1}, rate=\"gammaR\"))\n", - " self.add_reaction(Reaction(name=\"r12\", reactants={'Dr_prime': 1}, products={'A': 1, 'Dr': 1}, rate=\"thetaR\"))\n", - " self.add_reaction(Reaction(name=\"r13\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=\"alphaR\"))\n", - " self.add_reaction(Reaction(name=\"r14\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=\"alphaR_prime\"))\n", - " self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=\"deltaMR\"))\n", - " self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=\"betaR\"))\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 201, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model = VilarOscillator()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " solver = SSACSolver(model=model)\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":100,\n", - " # \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Exploration" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define simulator function (using gillespy2 wrapper)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from sciope.utilities.gillespy2 import wrapper\n", - "settings = configure_simulation()\n", - "simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['Da', 'Da_prime', 'Ma', 'Dr', 'Dr_prime', 'Mr', 'C', 'A', 'R'])\n", - "expression_array = wrapper.get_parameter_expression_array(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start local cluster using dask client" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from dask.distributed import Client\n", - "\n", - "c = Client()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define parameter sampler/design and summary statistics" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from sciope.designs import latin_hypercube_sampling\n", - "from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n", - "\n", - "lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n", - "lhc.generate_array(1000) # creates a LHD of size 1000\n", - "\n", - "# will use default minimal set of features\n", - "summary_stats = SummariesTSFRESH()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initiate StochMET" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from sciope.stochmet.stochmet import StochMET\n", - "\n", - "met = StochMET(simulator, lhc, summary_stats)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run parameter sweep" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "met.compute(n_points=500, chunk_size=10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore the result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# First lets add some appropiate information about the model and features\n", - "met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n", - "met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n", - "met.data.configurations['listOfSummaries'] = met.summaries.features\n", - "met.data.configurations['timepoints'] = model.tspan" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Here we use UMAP for dimension reduction\n", - "met.explore(dr_method='umap')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sciope.models.label_propagation import LPModel\n", - "# here lets use the dimension reduction embedding as input data\n", - "data = met.dr_model.embedding_\n", - "\n", - "model_lp = LPModel()\n", - "# train using basinhopping\n", - "model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# just to vislualize the result we will map the label distribution to the user_labels\n", - "# (will enable us to see the LP model output when using method 'explore')\n", - "user_labels = np.copy(met.data.user_labels)\n", - "# takes the label corresponding to index 0\n", - "met.data.user_labels = model_lp.model.label_distributions_[:, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "met.explore(dr_method='umap')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "met.data.user_labels = user_labels" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelInference.ipynb b/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelInference.ipynb deleted file mode 100644 index 7f8004ba54..0000000000 --- a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelInference.ipynb +++ /dev/null @@ -1,374 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n", - " EventTrigger, EventAssignment, RateRule, \\\n", - " AssignmentRule, FunctionDefinition\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import TauLeapingCSolver\n", - "# from gillespy2 import ODECSolver\n", - "# from gillespy2 import TauHybridSolver" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vilar_Oscillator" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "class VilarOscillator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Vilar_Oscillator\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alphaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"alphaA_prime\", expression=\"500\"))\n", - " self.add_parameter(Parameter(name=\"alphaR\", expression=\"0.01\"))\n", - " self.add_parameter(Parameter(name=\"alphaR_prime\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"betaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"betaR\", expression=\"5\"))\n", - " self.add_parameter(Parameter(name=\"deltaMA\", expression=\"10\"))\n", - " self.add_parameter(Parameter(name=\"deltaMR\", expression=\"0.5\"))\n", - " self.add_parameter(Parameter(name=\"deltaA\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"deltaR\", expression=\"0.2\"))\n", - " self.add_parameter(Parameter(name=\"gammaA\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"gammaR\", expression=\"1\"))\n", - " self.add_parameter(Parameter(name=\"gammaC\", expression=\"2\"))\n", - " self.add_parameter(Parameter(name=\"thetaA\", expression=\"50\"))\n", - " self.add_parameter(Parameter(name=\"thetaR\", expression=\"100\"))\n", - "\n", - " # Variables\n", - " self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"C\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"A\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"R\", initial_value=0, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r1\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=\"gammaC\"))\n", - " self.add_reaction(Reaction(name=\"r2\", reactants={'A': 1}, products={}, rate=\"deltaA\"))\n", - " self.add_reaction(Reaction(name=\"r3\", reactants={'C': 1}, products={'R': 1}, rate=\"deltaA\"))\n", - " self.add_reaction(Reaction(name=\"r4\", reactants={'R': 1}, products={}, rate=\"deltaR\"))\n", - " self.add_reaction(Reaction(name=\"r5\", reactants={'A': 1, 'Da': 1}, products={'Da_prime': 1}, rate=\"gammaA\"))\n", - " self.add_reaction(Reaction(name=\"r6\", reactants={'Da_prime': 1}, products={'A': 1, 'Da': 1}, rate=\"thetaA\"))\n", - " self.add_reaction(Reaction(name=\"r7\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=\"alphaA\"))\n", - " self.add_reaction(Reaction(name=\"r8\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=\"alphaA_prime\"))\n", - " self.add_reaction(Reaction(name=\"r9\", reactants={'Ma': 1}, products={}, rate=\"deltaMA\"))\n", - " self.add_reaction(Reaction(name=\"r10\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=\"betaA\"))\n", - " self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1, 'Dr': 1}, products={'Dr_prime': 1}, rate=\"gammaR\"))\n", - " self.add_reaction(Reaction(name=\"r12\", reactants={'Dr_prime': 1}, products={'A': 1, 'Dr': 1}, rate=\"thetaR\"))\n", - " self.add_reaction(Reaction(name=\"r13\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=\"alphaR\"))\n", - " self.add_reaction(Reaction(name=\"r14\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=\"alphaR_prime\"))\n", - " self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=\"deltaMR\"))\n", - " self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=\"betaR\"))\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 201, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model = VilarOscillator()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " solver = SSACSolver(model=model)\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":100,\n", - " # \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Inference" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from tsfresh.feature_extraction.settings import MinimalFCParameters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate some fixed(observed) data based on default parameters of the model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "fixed_data = model.run(**kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Reshape the data and remove timepoints array\n", - "fixed_data = fixed_data.to_array()\n", - "fixed_data = np.asarray([x.T for x in fixed_data])\n", - "fixed_data = fixed_data[:, 1:, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define prior distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from sciope.utilities.priors import uniform_prior\n", - "\n", - "# take default from mode 1 as reference\n", - "default_param = np.array(list(model.listOfParameters.items()))[:, 1]\n", - "\n", - "bound = []\n", - "for exp in default_param:\n", - " bound.append(float(exp.expression))\n", - "\n", - "# Set the bounds\n", - "bound = np.array(bound)\n", - "dmin = bound * 0.1\n", - "dmax = bound * 2.0\n", - "\n", - "# Here we use uniform prior\n", - "uni_prior = uniform_prior.UniformPrior(dmin, dmax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define simulator" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def get_variables(params, model):\n", - " # params - array, need to have the same order as model.listOfParameters\n", - " variables = {}\n", - " for e, pname in enumerate(model.listOfParameters.keys()):\n", - " variables[pname] = params[e]\n", - " return variables\n", - "\n", - "# Here we use the GillesPy2 Solver\n", - "def simulator(params, model):\n", - " variables = get_variables(params, model)\n", - "\n", - " res = model.run(**kwargs, variables=variables)\n", - " res = res.to_array()\n", - " tot_res = np.asarray([x.T for x in res]) # reshape to (N, S, T)\n", - " # should not contain timepoints\n", - " tot_res = tot_res[:, 1:, :]\n", - "\n", - " return tot_res\n", - "\n", - "# Wrapper, simulator function to abc should should only take one argument (the parameter point)\n", - "def simulator2(x):\n", - " return simulator(x, model=model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define summary statistics and distance function" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from sciope.utilities.summarystats import auto_tsfresh\n", - "from sciope.utilities.distancefunctions import naive_squared\n", - "\n", - "# Function to generate summary statistics\n", - "summ_func = auto_tsfresh.SummariesTSFRESH()\n", - "\n", - "# Distance\n", - "ns = naive_squared.NaiveSquaredDistance()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start local cluster using dask client" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from dask.distributed import Client\n", - "\n", - "c = Client()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Start abc instance" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from sciope.inference.abc_inference import ABC\n", - "\n", - "abc = ABC(fixed_data, sim=simulator2, prior_function=uni_prior, summaries_function=summ_func.compute, distance_function=ns)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# First compute the fixed(observed) mean\n", - "abc.compute_fixed_mean(chunk_size=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.8/site-packages/distributed/worker.py:3801: UserWarning: Large object of size 2.97 MiB detected in task graph: \n", - " (. Date: Fri, 19 Aug 2022 14:08:43 -0400 Subject: [PATCH 7/9] Updated the remaining spatial examples. --- .../Gene_Regulatory_Network_1.domn | 1 + .../Gene_Regulatory_Network_1.ipynb | 204 +++ .../Gene_Regulatory_Network_1.smdl | 1 + .../Gene_Regulatory_Network_2.ipynb | 206 +++ .../Gene_Regulatory_Network_2.smdl | 1 + .../Gene_Regulatory_network_2.domn | 1 + .../GeneRegulatoryNetwork1-checkpoint.ipynb | 158 --- .../GeneRegulatoryNetwork1.domn | 1 - .../GeneRegulatoryNetwork1.ipynb | 158 --- .../GeneRegulatoryNetwork1.smdl | 1 - .../GeneRegulatoryNetwork2.domn | 1 - .../GeneRegulatoryNetwork2.ipynb | 156 -- .../GeneRegulatoryNetwork2.smdl | 1 - .../PolarizationInYeast-checkpoint.ipynb | 1263 ----------------- .../PolarizationInYeast.ipynb | 130 -- .../PolarizationInYeast.smdl | 1 - .../Polarization_In_Yeast/Yeast_Cell.domn | 1 - .../Polarization_In_Yeast.domn | 1 + .../Polarization_In_Yeast.ipynb | 186 +++ .../Polarization_In_Yeast.smdl | 1 + 20 files changed, 602 insertions(+), 1871 deletions(-) create mode 100644 public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.domn create mode 100644 public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.ipynb create mode 100644 public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.smdl create mode 100644 public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.ipynb create mode 100644 public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.smdl create mode 100644 public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_network_2.domn delete mode 100644 public_models/Gene_Regulatory_Network/.ipynb_checkpoints/GeneRegulatoryNetwork1-checkpoint.ipynb delete mode 100644 public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.domn delete mode 100644 public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.ipynb delete mode 100644 public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.smdl delete mode 100644 public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.domn delete mode 100644 public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.ipynb delete mode 100644 public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.smdl delete mode 100644 public_models/Polarization_In_Yeast/.ipynb_checkpoints/PolarizationInYeast-checkpoint.ipynb delete mode 100644 public_models/Polarization_In_Yeast/PolarizationInYeast.ipynb delete mode 100644 public_models/Polarization_In_Yeast/PolarizationInYeast.smdl delete mode 100644 public_models/Polarization_In_Yeast/Yeast_Cell.domn create mode 100644 public_models/Yeast-Polarization.proj/Polarization_In_Yeast.domn create mode 100644 public_models/Yeast-Polarization.proj/Polarization_In_Yeast.wkgp/Polarization_In_Yeast.ipynb create mode 100644 public_models/Yeast-Polarization.proj/Polarization_In_Yeast.wkgp/Polarization_In_Yeast.smdl diff --git 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\ No newline at end of file diff --git a/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.ipynb b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.ipynb new file mode 100644 index 0000000000..e7dd161335 --- /dev/null +++ b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gene Regulatory Network 1\n", + "***\n", + "Here we illustrate how to implement the model describet in [Sturrock M, Hellander A, Matzavinos A, Chaplain MAJ. 2013 Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation. J R Soc Interface 10:20120988]\n", + "\n", + "This model uses the hes1 mesh (slide 66)\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import spatialpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Gene Regulatory Network 1 Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_gene_regulatory_network_1(parameter_values=None):\n", + " model = spatialpy.Model(name=\"Gene_Regulatory_Network_1\")\n", + "\n", + " model.NUCLEUS = 'Nucleus'\n", + " model.CYTOPLASM = 'Cytoplasm'\n", + "\n", + " # Domain\n", + " domain = spatialpy.Domain.read_stochss_domain('Gene_Regulatory_Network_1.smdl')\n", + " model.add_domain(domain)\n", + "\n", + " model.staticDomain = True\n", + "\n", + " # Variables\n", + " G_F = spatialpy.Species(name=\"G_F\", diffusion_coefficient=0)\n", + " G_O = spatialpy.Species(name=\"G_O\", diffusion_coefficient=0)\n", + " P = spatialpy.Species(name=\"P\", diffusion_coefficient=1e-12)\n", + " mRNA = spatialpy.Species(name=\"mRNA\", diffusion_coefficient=1e-12)\n", + " model.add_species([G_F, G_O, P, mRNA])\n", + "\n", + " # Initial Conditions\n", + " G_F_ic = spatialpy.PlaceInitialCondition(species=\"G_F\", count=1, location=[0, 0, 0])\n", + " model.add_initial_condition(G_F_ic)\n", + "\n", + " # Parameters\n", + " mu = spatialpy.Parameter(name=\"mu\", expression=\"0.002\")\n", + " kappa = spatialpy.Parameter(name=\"kappa\", expression=\"0.015\")\n", + " ka = spatialpy.Parameter(name=\"ka\", expression=\"1.6605778811e-20\")\n", + " kd = spatialpy.Parameter(name=\"kd\", expression=\"0.01\")\n", + " gamma_m = spatialpy.Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", + " gamma_p = spatialpy.Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", + " model.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", + "\n", + " # Reactions\n", + " R1 = spatialpy.Reaction(\n", + " name=\"R1\", rate=\"ka\",\n", + " reactants={'G_F': 1, 'P': 1}, products={'G_O': 1}\n", + " )\n", + " R2 = spatialpy.Reaction(\n", + " name=\"R2\", rate=\"kappa\", restrict_to=[model.CYTOPLASM],\n", + " reactants={'mRNA': 1}, products={'mRNA': 1, 'P': 1}\n", + " )\n", + " R3 = spatialpy.Reaction(\n", + " name=\"R3\", rate=\"mu\",\n", + " reactants={'G_F': 1}, products={'G_F': 1, 'mRNA': 1}\n", + " )\n", + " R4 = spatialpy.Reaction(\n", + " name=\"R4\", rate=\"gamma_m\",\n", + " reactants={'mRNA': 1}, products={}\n", + " )\n", + " R5 = spatialpy.Reaction(\n", + " name=\"R5\", rate=\"gamma_p\",\n", + " reactants={'P': 1}, products={}\n", + " )\n", + " R6 = spatialpy.Reaction(\n", + " name=\"R6\", rate=\"kd\",\n", + " reactants={'G_O': 1}, products={'G_F': 1, 'P': 1}\n", + " )\n", + " model.add_reaction([R1, R2, R3, R4, R5, R6])\n", + "\n", + " # Timespan\n", + " tspan = spatialpy.TimeSpan.arange(43.221610805402705, t=86400, timestep_size=1)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_gene_regulatory_network_1()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('G_F', animated=True, width=None, height=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.smdl b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.smdl new file mode 100644 index 0000000000..f99cdbd089 --- /dev/null +++ b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_1.wkgp/Gene_Regulatory_Network_1.smdl @@ -0,0 +1 @@ +{"is_spatial":true,"defaultID":24,"defaultMode":"discrete","annotation":"Here we illustrate how to implement the model describet in [Sturrock M, Hellander A, Matzavinos A, Chaplain MAJ. 2013 Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation. J R Soc Interface 10:20120988]\n\nThis model uses the hes1 mesh (slide 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\ No newline at end of file diff --git a/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.ipynb b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.ipynb new file mode 100644 index 0000000000..9e6c67e0a5 --- /dev/null +++ b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gene Regulatory Network 2\n", + "***\n", + "Here we illustrate how to implement the model describet in [Sturrock M, Hellander A, Matzavinos A, Chaplain MAJ. 2013 Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation. J R Soc Interface 10:20120988]\n", + "\n", + "This model uses the cell_coarse mesh (slide 65)\n", + " - This mesh contains 2 types\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import spatialpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Gene Regulatory Network 2 Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_gene_regulatory_network_2(parameter_values=None):\n", + " model = spatialpy.Model(name=\"Gene_Regulatory_Network_2\")\n", + "\n", + " # Types\n", + " model.NUCLEUS = 'Nucleus'\n", + " model.CYTOPLASM = 'Cytoplasm'\n", + " \n", + " # Domain\n", + " domain = spatialpy.Domain.read_stochss_domain('Gene_Regulatory_Network_2.smdl')\n", + " model.add_domain(domain)\n", + "\n", + " model.staticDomain = True\n", + "\n", + " # Variables\n", + " G_F = spatialpy.Species(name=\"G_F\", diffusion_coefficient=0)\n", + " G_O = spatialpy.Species(name=\"G_O\", diffusion_coefficient=0)\n", + " P = spatialpy.Species(name=\"P\", diffusion_coefficient=1e-12)\n", + " mRNA = spatialpy.Species(name=\"mRNA\", diffusion_coefficient=1e-12)\n", + " model.add_species([G_F, G_O, P, mRNA])\n", + "\n", + " # Initial Conditions\n", + " G_F_ic = spatialpy.PlaceInitialCondition(species=\"G_F\", count=1, location=[0, 0, 0])\n", + " model.add_initial_condition(G_F_ic)\n", + "\n", + " # Parameters\n", + " mu = spatialpy.Parameter(name=\"mu\", expression=\"0.002\")\n", + " kappa = spatialpy.Parameter(name=\"kappa\", expression=\"0.015\")\n", + " ka = spatialpy.Parameter(name=\"ka\", expression=\"1.6605778811e-20\")\n", + " kd = spatialpy.Parameter(name=\"kd\", expression=\"0.01\")\n", + " gamma_m = spatialpy.Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", + " gamma_p = spatialpy.Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", + " model.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", + "\n", + " # Reactions\n", + " R1 = spatialpy.Reaction(\n", + " name=\"R1\", rate=\"ka\",\n", + " reactants={'G_F': 1, 'P': 1}, products={'G_O': 1}\n", + " )\n", + " R2 = spatialpy.Reaction(\n", + " name=\"R2\", rate=\"kappa\", restrict_to=[model.CYTOPLASM],\n", + " reactants={'mRNA': 1}, products={'mRNA': 1, 'P': 1}\n", + " )\n", + " R3 = spatialpy.Reaction(\n", + " name=\"R3\", rate=\"mu\",\n", + " reactants={'G_F': 1}, products={'G_F': 1, 'mRNA': 1}\n", + " )\n", + " R4 = spatialpy.Reaction(\n", + " name=\"R4\", rate=\"gamma_m\",\n", + " reactants={'mRNA': 1}, products={}\n", + " )\n", + " R5 = spatialpy.Reaction(\n", + " name=\"R5\", rate=\"gamma_p\",\n", + " reactants={'P': 1}, products={}\n", + " )\n", + " R6 = spatialpy.Reaction(\n", + " name=\"R6\", rate=\"kd\",\n", + " reactants={'G_O': 1}, products={'G_F': 1, 'P': 1}\n", + " )\n", + " model.add_reaction([R1, R2, R3, R4, R5, R6])\n", + "\n", + " # Timespan\n", + " tspan = spatialpy.TimeSpan.arange(43.221610805402705, t=86400, timestep_size=1)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_gene_regulatory_network_2()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('G_F', animated=True, width=None, height=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.smdl b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.smdl new file mode 100644 index 0000000000..47ce80350a --- /dev/null +++ b/public_models/Gene-Regulatory-Network.proj/Gene_Regulatory_Network_2.wkgp/Gene_Regulatory_Network_2.smdl @@ -0,0 +1 @@ +{"is_spatial":true,"defaultID":23,"defaultMode":"discrete","annotation":"Here we illustrate how to implement the model describet in [Sturrock M, Hellander A, Matzavinos A, Chaplain MAJ. 2013 Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation. J R Soc Interface 10:20120988]\n\nThis model uses the cell_coarse mesh (slide 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\ No newline at end of file diff --git a/public_models/Gene_Regulatory_Network/.ipynb_checkpoints/GeneRegulatoryNetwork1-checkpoint.ipynb b/public_models/Gene_Regulatory_Network/.ipynb_checkpoints/GeneRegulatoryNetwork1-checkpoint.ipynb deleted file mode 100644 index 27696ed63f..0000000000 --- a/public_models/Gene_Regulatory_Network/.ipynb_checkpoints/GeneRegulatoryNetwork1-checkpoint.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import spatialpy\n", - "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", - " PlaceInitialCondition, \\\n", - " UniformInitialCondition, \\\n", - " ScatterInitialCondition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GeneRegulatoryNetwork1" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class GeneRegulatoryNetwork1(Model):\n", - " def __init__(self):\n", - " Model.__init__(self, name=\"GeneRegulatoryNetwork1\")\n", - "\n", - " # Domain\n", - " mesh = Mesh.read_stochss_domain('GeneRegulatoryNetwork1.smdl')\n", - " self.add_mesh(mesh)\n", - "\n", - " self.staticDomain = True\n", - "\n", - " # Variables\n", - " G_F = Species(name=\"G_F\", diffusion_constant=1e-12)\n", - " G_O = Species(name=\"G_O\", diffusion_constant=1e-12)\n", - " P = Species(name=\"P\", diffusion_constant=1e-12)\n", - " mRNA = Species(name=\"mRNA\", diffusion_constant=1e-12)\n", - " self.add_species([G_F, G_O, P, mRNA])\n", - "\n", - " self.restrict(G_F, [])\n", - " self.restrict(G_O, [])\n", - " self.restrict(P, [1, 2])\n", - " self.restrict(mRNA, [1, 2])\n", - "\n", - " # Initial Conditions\n", - " self.add_initial_condition(PlaceInitialCondition(species=G_F, count=1, location=[0, 0, 0]))\n", - "\n", - " # Parameters\n", - " mu = Parameter(name=\"mu\", expression=\"0.002\")\n", - " kappa = Parameter(name=\"kappa\", expression=\"0.015\")\n", - " ka = Parameter(name=\"ka\", expression=\"10000000\")\n", - " kd = Parameter(name=\"kd\", expression=\"0.01\")\n", - " gamma_m = Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", - " gamma_p = Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", - " self.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", - "\n", - " # Reactions\n", - " r1 = Reaction(name=\"r1\", reactants={G_F: 1}, products={G_F: 1, mRNA: 1}, rate=self.listOfParameters[\"mu\"])\n", - " r2 = Reaction(name=\"r2\", reactants={G_O: 1}, products={G_F: 1, P: 1}, rate=self.listOfParameters[\"ka\"])\n", - " r3 = Reaction(name=\"r3\", reactants={G_F: 1, P: 1}, products={G_O: 1}, rate=self.listOfParameters[\"kd\"])\n", - " r4 = Reaction(name=\"r4\", restrict_to=[2], reactants={mRNA: 1}, products={mRNA: 1, P: 1}, rate=self.listOfParameters[\"kappa\"])\n", - " r5 = Reaction(name=\"r5\", reactants={mRNA: 1}, products={}, rate=self.listOfParameters[\"gamma_m\"])\n", - " r6 = Reaction(name=\"r6\", reactants={P: 1}, products={}, rate=self.listOfParameters[\"gamma_p\"])\n", - " self.add_reaction([r1, r2, r3, r4, r5, r6])\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 1000, 10), timestep_size=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = GeneRegulatoryNetwork1()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " # \"number_of_trajectories\":1,\n", - " # \"seed\":None,\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.plot_species('G_F', animated=True, width=None, height=None)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.domn b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.domn deleted file mode 100644 index 7fa0699edb..0000000000 --- a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.domn +++ /dev/null @@ -1 +0,0 @@ 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\ No newline at end of file diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.ipynb b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.ipynb deleted file mode 100644 index 27696ed63f..0000000000 --- a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import spatialpy\n", - "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", - " PlaceInitialCondition, \\\n", - " UniformInitialCondition, \\\n", - " ScatterInitialCondition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GeneRegulatoryNetwork1" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class GeneRegulatoryNetwork1(Model):\n", - " def __init__(self):\n", - " Model.__init__(self, name=\"GeneRegulatoryNetwork1\")\n", - "\n", - " # Domain\n", - " mesh = Mesh.read_stochss_domain('GeneRegulatoryNetwork1.smdl')\n", - " self.add_mesh(mesh)\n", - "\n", - " self.staticDomain = True\n", - "\n", - " # Variables\n", - " G_F = Species(name=\"G_F\", diffusion_constant=1e-12)\n", - " G_O = Species(name=\"G_O\", diffusion_constant=1e-12)\n", - " P = Species(name=\"P\", diffusion_constant=1e-12)\n", - " mRNA = Species(name=\"mRNA\", diffusion_constant=1e-12)\n", - " self.add_species([G_F, G_O, P, mRNA])\n", - "\n", - " self.restrict(G_F, [])\n", - " self.restrict(G_O, [])\n", - " self.restrict(P, [1, 2])\n", - " self.restrict(mRNA, [1, 2])\n", - "\n", - " # Initial Conditions\n", - " self.add_initial_condition(PlaceInitialCondition(species=G_F, count=1, location=[0, 0, 0]))\n", - "\n", - " # Parameters\n", - " mu = Parameter(name=\"mu\", expression=\"0.002\")\n", - " kappa = Parameter(name=\"kappa\", expression=\"0.015\")\n", - " ka = Parameter(name=\"ka\", expression=\"10000000\")\n", - " kd = Parameter(name=\"kd\", expression=\"0.01\")\n", - " gamma_m = Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", - " gamma_p = Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", - " self.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", - "\n", - " # Reactions\n", - " r1 = Reaction(name=\"r1\", reactants={G_F: 1}, products={G_F: 1, mRNA: 1}, rate=self.listOfParameters[\"mu\"])\n", - " r2 = Reaction(name=\"r2\", reactants={G_O: 1}, products={G_F: 1, P: 1}, rate=self.listOfParameters[\"ka\"])\n", - " r3 = Reaction(name=\"r3\", reactants={G_F: 1, P: 1}, products={G_O: 1}, rate=self.listOfParameters[\"kd\"])\n", - " r4 = Reaction(name=\"r4\", restrict_to=[2], reactants={mRNA: 1}, products={mRNA: 1, P: 1}, rate=self.listOfParameters[\"kappa\"])\n", - " r5 = Reaction(name=\"r5\", reactants={mRNA: 1}, products={}, rate=self.listOfParameters[\"gamma_m\"])\n", - " r6 = Reaction(name=\"r6\", reactants={P: 1}, products={}, rate=self.listOfParameters[\"gamma_p\"])\n", - " self.add_reaction([r1, r2, r3, r4, r5, r6])\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 1000, 10), timestep_size=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = GeneRegulatoryNetwork1()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " # \"number_of_trajectories\":1,\n", - " # \"seed\":None,\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.plot_species('G_F', animated=True, width=None, height=None)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.smdl b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.smdl deleted file mode 100644 index e6c01014fd..0000000000 --- a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.smdl +++ /dev/null @@ -1 +0,0 @@ -{"is_spatial":true,"defaultID":18,"defaultMode":"discrete","annotation":"This model uses the hes1 mesh 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\ No newline at end of file diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.ipynb b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.ipynb deleted file mode 100644 index e93d8b2df8..0000000000 --- a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.ipynb +++ /dev/null @@ -1,156 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import spatialpy\n", - "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", - " PlaceInitialCondition, \\\n", - " UniformInitialCondition, \\\n", - " ScatterInitialCondition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GeneRegulatoryNetwork2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class GeneRegulatoryNetwork2(Model):\n", - " def __init__(self):\n", - " Model.__init__(self, name=\"GeneRegulatoryNetwork2\")\n", - "\n", - " # Domain\n", - " mesh = Mesh.read_stochss_domain('GeneRegulatoryNetwork2.smdl')\n", - " self.add_mesh(mesh)\n", - "\n", - " self.staticDomain = True\n", - "\n", - " # Variables\n", - " G_F = Species(name=\"G_F\", diffusion_constant=1e-12)\n", - " G_O = Species(name=\"G_O\", diffusion_constant=1e-12)\n", - " P = Species(name=\"P\", diffusion_constant=1e-12)\n", - " mRNA = Species(name=\"mRNA\", diffusion_constant=1e-12)\n", - " self.add_species([G_F, G_O, P, mRNA])\n", - "\n", - " self.restrict(G_F, [])\n", - " self.restrict(G_O, [])\n", - "\n", - " # Initial Conditions\n", - " self.add_initial_condition(PlaceInitialCondition(species=G_F, count=1, location=[0, 0, 0]))\n", - "\n", - " # Parameters\n", - " mu = Parameter(name=\"mu\", expression=\"0.002\")\n", - " kappa = Parameter(name=\"kappa\", expression=\"0.015\")\n", - " ka = Parameter(name=\"ka\", expression=\"10000000\")\n", - " kd = Parameter(name=\"kd\", expression=\"0.01\")\n", - " gamma_m = Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", - " gamma_p = Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", - " self.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", - "\n", - " # Reactions\n", - " r1 = Reaction(name=\"r1\", reactants={G_F: 1}, products={G_F: 1, mRNA: 1}, rate=self.listOfParameters[\"mu\"])\n", - " r2 = Reaction(name=\"r2\", reactants={G_O: 1}, products={G_F: 1, P: 1}, rate=self.listOfParameters[\"ka\"])\n", - " r3 = Reaction(name=\"r3\", reactants={G_F: 1, P: 1}, products={G_O: 1}, rate=self.listOfParameters[\"kd\"])\n", - " r4 = Reaction(name=\"r4\", restrict_to=[2], reactants={mRNA: 1}, products={mRNA: 1, P: 1}, rate=self.listOfParameters[\"kappa\"])\n", - " r5 = Reaction(name=\"r5\", reactants={mRNA: 1}, products={}, rate=self.listOfParameters[\"gamma_m\"])\n", - " r6 = Reaction(name=\"r6\", reactants={P: 1}, products={}, rate=self.listOfParameters[\"gamma_p\"])\n", - " self.add_reaction([r1, r2, r3, r4, r5, r6])\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 1000, 10), timestep_size=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = GeneRegulatoryNetwork2()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " # \"number_of_trajectories\":1,\n", - " # \"seed\":None,\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.plot_species('G_F', animated=True, width=None, height=None)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.smdl b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.smdl deleted file mode 100644 index 47429ec9a1..0000000000 --- a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.smdl +++ /dev/null @@ -1 +0,0 @@ -{"is_spatial":true,"defaultID":17,"defaultMode":"","annotation":"This model uses the cell_coarse mesh (slide 65)\n - This mesh contains 2 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b/public_models/Polarization_In_Yeast/.ipynb_checkpoints/PolarizationInYeast-checkpoint.ipynb deleted file mode 100644 index 669f32b3ae..0000000000 --- a/public_models/Polarization_In_Yeast/.ipynb_checkpoints/PolarizationInYeast-checkpoint.ipynb +++ /dev/null @@ -1,1263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import spatialpy\n", - "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", - " PlaceInitialCondition, \\\n", - " UniformInitialCondition, \\\n", - " ScatterInitialCondition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PolarizationInYeast" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class PolarizationInYeast(Model):\n", - " def __init__(self):\n", - " Model.__init__(self, name=\"PolarizationInYeast\")\n", - "\n", - " # Domain\n", - " mesh = Mesh.read_stochss_domain('PolarizationInYeast.smdl')\n", - " self.add_mesh(mesh)\n", - "\n", - " self.staticDomain = True\n", - "\n", - " # Variables\n", - " Cdc42c = Species(name=\"Cdc42c\", diffusion_constant=10)\n", - " Cdc42m = Species(name=\"Cdc42m\", diffusion_constant=0.0053)\n", - " self.add_species([Cdc42c, Cdc42m])\n", - "\n", - " self.restrict(Cdc42m, [2])\n", - "\n", - " # Initial Conditions\n", - " self.add_initial_condition(ScatterInitialCondition(species=Cdc42c, count=300, types=[1]))\n", - " self.add_initial_condition(ScatterInitialCondition(species=Cdc42m, count=100, types=[2]))\n", - "\n", - " # Parameters\n", - " kon = Parameter(name=\"kon\", expression=\"1.6666666667e-05\")\n", - " koff = Parameter(name=\"koff\", expression=\"0.15\")\n", - " kfb = Parameter(name=\"kfb\", expression=\"0.166666666666667\")\n", - " self.add_parameter([kon, koff, kfb])\n", - "\n", - " # Reactions\n", - " r1 = Reaction(name=\"r1\", reactants={Cdc42c: 1}, products={Cdc42m: 1}, rate=self.listOfParameters[\"kon\"])\n", - " r2 = Reaction(name=\"r2\", reactants={Cdc42m: 1}, products={Cdc42c: 1}, rate=self.listOfParameters[\"koff\"])\n", - " r3 = Reaction(name=\"r3\", restrict_to=[2], reactants={Cdc42c: 1, Cdc42m: 1}, products={Cdc42m: 2}, rate=self.listOfParameters[\"kfb\"])\n", - " self.add_reaction([r1, r2, r3])\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 5000, 10), timestep_size=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = PolarizationInYeast()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " # \"number_of_trajectories\":1,\n", - " # \"seed\":None,\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results.plot_species('Cdc42c', animated=True, width=None, height=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PolarizationInYeast\n", - "\n", - "**********\n", - "Species\n", - "**********\n", - "\n", - "Cdc42c: 10\n", - "Cdc42m: 0.0053\n", - "\n", - "**********\n", - "Initial Conditions\n", - "**********\n", - "\n", - "Cdc42c: 300, Scatter over: [1]\n", - "Cdc42m: 100, Scatter over: [2]\n", - "\n", - "**********\n", - "Diffusion Restrictions\n", - "**********\n", - "\n", - "Cdc42m is restricted to: [2]\n", - "\n", - "**********\n", - "Parameters\n", - "**********\n", - "\n", - "kon: 1.6666666667e-05\n", - "koff: 0.15\n", - "kfb: 0.166666666666667\n", - "\n", - "**********\n", - "Reactions\n", - "**********\n", - "\n", - "r1, Active in: None\n", - "\tReactants\n", - "\t\tCdc42c: 1\n", - "\tProducts\n", - "\t\tCdc42m: 1\n", - "\tPropensity Function: kon*Cdc42c\n", - "r2, Active in: None\n", - "\tReactants\n", - "\t\tCdc42m: 1\n", - "\tProducts\n", - "\t\tCdc42c: 1\n", - "\tPropensity Function: koff*Cdc42m\n", - "r3, Active in: [2]\n", - "\tReactants\n", - "\t\tCdc42c: 1\n", - "\t\tCdc42m: 1\n", - "\tProducts\n", - "\t\tCdc42m: 2\n", - "\tPropensity Function: kfb*Cdc42c*Cdc42m/vol\n", - "\n", - "**********\n", - "Domain\n", - "**********\n", - "\n", - "Domain Attributes\n", - "\n", - " On Boundary: None\n", - " Mesh Size: None\n", - " RHO_0: 2\n", - " C_0: 10\n", - " P_0: 10\n", - " Gravity: [0, 0, 0]\n", - " X Limit: (-0.9985541604477006, 0.9947062116569865)\n", - " Y Limit: (-0.9923297008495786, 0.9902528598554616)\n", - " Z Limit: (-0.9929082749587046, 1)\n", - "\n", - "Paritcles\n", - "\n", - " 1: [0.39194193 0.56843369 0.34968195]\n", - " Volume:0.01481721050895814, Mass: 0.01481721050895814, Type: 1, nu: 0.0, Fixed: False\n", - " 2: [0.51449576 0.51449576 0.68599434]\n", - " Volume:0.004877171985296613, Mass: 0.004877171985296613, Type: 2, nu: 0.0, Fixed: False\n", - " 3: [0.52621468 0.69882514 0.47425753]\n", - " Volume:0.0038392863067038038, Mass: 0.0038392863067038038, Type: 2, nu: 0.0, Fixed: False\n", - " 4: [0.29252731 0.45444123 0.60844657]\n", - " Volume:0.009527576304879382, Mass: 0.009527576304879382, Type: 1, nu: 0.0, Fixed: False\n", - " 5: [-0.67432165 0.21588739 0.38567391]\n", - " Volume:0.01067546259542419, Mass: 0.01067546259542419, Type: 1, nu: 0.0, Fixed: False\n", - " 6: [-0.62560362 -0.09998057 0.21092954]\n", - " Volume:0.013843585084655025, Mass: 0.013843585084655025, Type: 1, nu: 0.0, Fixed: 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Fixed: False\n" - ] - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/public_models/Polarization_In_Yeast/PolarizationInYeast.ipynb b/public_models/Polarization_In_Yeast/PolarizationInYeast.ipynb deleted file mode 100644 index 76d542b1ee..0000000000 --- a/public_models/Polarization_In_Yeast/PolarizationInYeast.ipynb +++ /dev/null @@ -1,130 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import spatialpy\n", - "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", - " PlaceInitialCondition, \\\n", - " UniformInitialCondition, \\\n", - " ScatterInitialCondition" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PolarizationInYeast" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class PolarizationInYeast(Model):\n", - " def __init__(self):\n", - " Model.__init__(self, name=\"PolarizationInYeast\")\n", - "\n", - " # Domain\n", - " mesh = Mesh.read_stochss_domain('PolarizationInYeast.smdl')\n", - " self.add_mesh(mesh)\n", - "\n", - " self.staticDomain = True\n", - "\n", - " # Variables\n", - " Cdc42c = Species(name=\"Cdc42c\", diffusion_constant=10)\n", - " Cdc42m = Species(name=\"Cdc42m\", diffusion_constant=0.0053)\n", - " self.add_species([Cdc42c, Cdc42m])\n", - "\n", - " self.restrict(Cdc42m, [2])\n", - "\n", - " # Initial Conditions\n", - " self.add_initial_condition(ScatterInitialCondition(species=Cdc42c, count=300, types=[1]))\n", - " self.add_initial_condition(ScatterInitialCondition(species=Cdc42m, count=100, types=[2]))\n", - "\n", - " # Parameters\n", - " kon = Parameter(name=\"kon\", expression=\"1.6666666667e-05\")\n", - " koff = Parameter(name=\"koff\", expression=\"0.15\")\n", - " kfb = Parameter(name=\"kfb\", expression=\"0.166666666666667\")\n", - " self.add_parameter([kon, koff, kfb])\n", - "\n", - " # Reactions\n", - " r1 = Reaction(name=\"r1\", reactants={Cdc42c: 1}, products={Cdc42m: 1}, rate=self.listOfParameters[\"kon\"])\n", - " r2 = Reaction(name=\"r2\", reactants={Cdc42m: 1}, products={Cdc42c: 1}, rate=self.listOfParameters[\"koff\"])\n", - " r3 = Reaction(name=\"r3\", restrict_to=[2], reactants={Cdc42c: 1, Cdc42m: 1}, products={Cdc42m: 2}, rate=self.listOfParameters[\"kfb\"])\n", - " self.add_reaction([r1, r2, r3])\n", - "\n", - " # Timespan\n", - " self.timespan(np.arange(0, 5000, 10), timestep_size=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = PolarizationInYeast()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " # \"number_of_trajectories\":1,\n", - " # \"seed\":None,\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - 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\ No newline at end of file diff --git a/public_models/Yeast-Polarization.proj/Polarization_In_Yeast.wkgp/Polarization_In_Yeast.ipynb b/public_models/Yeast-Polarization.proj/Polarization_In_Yeast.wkgp/Polarization_In_Yeast.ipynb new file mode 100644 index 0000000000..654c4ba8c1 --- /dev/null +++ b/public_models/Yeast-Polarization.proj/Polarization_In_Yeast.wkgp/Polarization_In_Yeast.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Polarization In Yeast\n", + "***\n", + "Reactions could not be completed from the slides (74 - 82)\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import spatialpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Polarization In Yeast Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_polarization_in_yeast(parameter_values=None):\n", + " model = spatialpy.Model(name=\"Polarization_In_Yeast\")\n", + "\n", + " model.CYTOPLASM = 'Cytoplasm'\n", + " model.MEMBRANE = 'Membrane'\n", + "\n", + " # Domain\n", + " domain = spatialpy.Domain.read_stochss_domain('Polarization_In_Yeast.smdl')\n", + " model.add_domain(domain)\n", + "\n", + " model.staticDomain = True\n", + "\n", + " # Variables\n", + " Cdc42c = spatialpy.Species(name=\"Cdc42c\", diffusion_coefficient=10)\n", + " Cdc42m = spatialpy.Species(name=\"Cdc42m\", diffusion_coefficient=0.0053, restrict_to=[model.MEMBRANE])\n", + " model.add_species([Cdc42c, Cdc42m])\n", + "\n", + " # Initial Conditions\n", + " Cdc42c_ic = spatialpy.ScatterInitialCondition(species=\"Cdc42c\", count=300, types=[model.CYTOPLASM])\n", + " Cdc42m_ic = spatialpy.ScatterInitialCondition(species=\"Cdc42m\", count=100, types=[model.MEMBRANE])\n", + " model.add_initial_condition([Cdc42c_ic, Cdc42m_ic])\n", + "\n", + " # Parameters\n", + " kon = spatialpy.Parameter(name=\"kon\", expression=\"1.6666666667e-05\")\n", + " koff = spatialpy.Parameter(name=\"koff\", expression=\"0.15\")\n", + " kfb = spatialpy.Parameter(name=\"kfb\", expression=\"0.166666666666667\")\n", + " model.add_parameter([kon, koff, kfb])\n", + "\n", + " # Reactions\n", + " r1 = spatialpy.Reaction(\n", + " name=\"r1\", rate=\"kon\",\n", + " reactants={'Cdc42c': 1}, products={'Cdc42m': 1}\n", + " )\n", + " r2 = spatialpy.Reaction(\n", + " name=\"r2\", rate=\"koff\",\n", + " reactants={'Cdc42m': 1}, products={'Cdc42c': 1}\n", + " )\n", + " r3 = spatialpy.Reaction(\n", + " name=\"r3\", rate=\"kfb\", restrict_to=[model.MEMBRANE],\n", + " reactants={'Cdc42c': 1, 'Cdc42m': 1}, products={'Cdc42m': 2}\n", + " )\n", + " model.add_reaction([r1, r2, r3])\n", + "\n", + " # Timespan\n", + " tspan = spatialpy.TimeSpan.arange(10, t=5000, timestep_size=1)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_polarization_in_yeast()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('Cdc42c', animated=True, width=None, height=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Yeast-Polarization.proj/Polarization_In_Yeast.wkgp/Polarization_In_Yeast.smdl b/public_models/Yeast-Polarization.proj/Polarization_In_Yeast.wkgp/Polarization_In_Yeast.smdl new file mode 100644 index 0000000000..76560f70ca --- /dev/null +++ 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\ No newline at end of file From 8b2d7e56d6e18e991c6b88a3e854bfac58970999 Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Fri, 19 Aug 2022 14:09:34 -0400 Subject: [PATCH 8/9] Added the spatialpy birth-death example. --- .../Spatial_Birth_Death.domn | 1 + .../Spatial_Birth_Death.ipynb | 176 ++++++++++++++++++ .../Spatial_Birth_Death.smdl | 1 + 3 files changed, 178 insertions(+) create mode 100644 public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.domn create mode 100644 public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.ipynb create mode 100644 public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.smdl diff --git a/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.domn b/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.domn new file mode 100644 index 0000000000..f5fd95f807 --- /dev/null +++ b/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.domn @@ -0,0 +1 @@ +{"boundary_condition":{"reflect_x":true,"reflect_y":true,"reflect_z":true},"c_0":10,"gravity":[0,0,0],"p_0":100,"rho_0":1,"size":null,"x_lim":[0,1],"y_lim":[0,1],"z_lim":[0,0],"static":true,"template_version":1,"types":[{"c":10,"fixed":false,"mass":1,"name":"Un-Assigned","nu":0,"rho":1,"typeID":0,"volume":1},{"c":10,"fixed":true,"geometry":"","mass":1,"name":"Habitat","nu":0,"rho":1,"typeID":1,"volume":1}],"particles":[{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":0,"point":[0,0,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":1,"point":[0,0.1111111111111111,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":2,"point":[0,0.2222222222222222,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":3,"point":[0,0.3333333333333333,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":4,"point":[0,0.4444444444444444,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":5,"point":[0,0.5555555555555556,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":6,"point":[0,0.6666666666666666,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":7,"point":[0,0.7777777777777777,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":8,"point":[0,0.8888888888888888,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":9,"point":[0,1,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":11,"point":[0.1111111111111111,0,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":12,"point":[0.1111111111111111,0.1111111111111111,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":13,"point":[0.1111111111111111,0.2222222222222222,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":14,"point":[0.1111111111111111,0.3333333333333333,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":15,"point":[0.1111111111111111,0.4444444444444444,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":16,"point":[0.1111111111111111,0.5555555555555556,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":17,"point":[0.1111111111111111,0.6666666666666666,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":18,"point":[0.1111111111111111,0.7777777777777777,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":19,"point":[0.1111111111111111,0.8888888888888888,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":20,"point":[0.1111111111111111,1,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":22,"point":[0.2222222222222222,0,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":23,"point":[0.2222222222222222,0.1111111111111111,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":24,"point":[0.2222222222222222,0.2222222222222222,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":25,"point":[0.2222222222222222,0.3333333333333333,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":26,"point":[0.2222222222222222,0.4444444444444444,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":27,"point":[0.2222222222222222,0.5555555555555556,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":28,"point":[0.2222222222222222,0.6666666666666666,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":29,"point":[0.2222222222222222,0.7777777777777777,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":30,"point":[0.2222222222222222,0.8888888888888888,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":31,"point":[0.2222222222222222,1,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":33,"point":[0.3333333333333333,0,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":34,"point":[0.3333333333333333,0.1111111111111111,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":35,"point":[0.3333333333333333,0.2222222222222222,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":36,"point":[0.3333333333333333,0.3333333333333333,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":37,"point":[0.3333333333333333,0.4444444444444444,0],"rho":1,"type":1,"volume":0.01},{"c":10,"fixed":true,"mass":1,"nu":0,"particle_id":38,"point":[0.3333333333333333,0.5555555555555556,0],"rho":1,"ty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\ No newline at end of file diff --git a/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.ipynb b/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.ipynb new file mode 100644 index 0000000000..e010400bc8 --- /dev/null +++ b/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spatial Birth Death\n", + "***\n", + "## Setup the Environment\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import spatialpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Create the Spatial Birth Death Model\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_spatial_birth_death(parameter_values=None):\n", + " model = spatialpy.Model(name=\"Spatial_Birth_Death\")\n", + "\n", + " model.HABITAT = 'Habitat'\n", + "\n", + " # Domain\n", + " domain = spatialpy.Domain.read_stochss_domain('Spatial_Birth_Death.smdl')\n", + " model.add_domain(domain)\n", + " \n", + " model.staticDomain = True\n", + "\n", + " # Variables\n", + " Rabbit = spatialpy.Species(name=\"Rabbit\", diffusion_coefficient=0.1)\n", + " model.add_species([Rabbit])\n", + "\n", + " # Initial Conditions\n", + " Rabbit_ic = spatialpy.ScatterInitialCondition(species=\"Rabbit\", count=100)\n", + " model.add_initial_condition(Rabbit_ic)\n", + "\n", + " # Parameters\n", + " k_birth = spatialpy.Parameter(name=\"k_birth\", expression=\"10\")\n", + " k_death = spatialpy.Parameter(name=\"k_death\", expression=\"0.1\")\n", + " model.add_parameter([k_birth, k_death])\n", + "\n", + " # Reactions\n", + " birth = spatialpy.Reaction(\n", + " name=\"birth\", rate=\"k_birth\",\n", + " reactants={}, products={'Rabbit': 1}\n", + " )\n", + " death = spatialpy.Reaction(\n", + " name=\"death\", rate=\"k_death\",\n", + " reactants={'Rabbit': 1}, products={}\n", + " )\n", + " model.add_reaction([birth, death])\n", + "\n", + " # Timespan\n", + " tspan = spatialpy.TimeSpan.arange(1, t=10, timestep_size=1)\n", + " model.timespan(tspan)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiate the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = create_spatial_birth_death()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Simulation Parameters\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Run the Simulation\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "## Visualization\n", + "***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('Rabbit', animated=True, width=None, height=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.smdl b/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.smdl new file mode 100644 index 0000000000..ba0aa81da2 --- /dev/null +++ b/public_models/Spatial-Birth-Death.proj/Spatial_Birth_Death.wkgp/Spatial_Birth_Death.smdl @@ -0,0 +1 @@ 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\ No newline at end of file From 39d6b41036e6e76ab8607cc3c02dd3a618f964e8 Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Fri, 19 Aug 2022 15:14:22 -0400 Subject: [PATCH 9/9] Fixed conversion function for boundary conditions. --- stochss/handlers/util/stochss_spatial_model.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/stochss/handlers/util/stochss_spatial_model.py b/stochss/handlers/util/stochss_spatial_model.py index 9ce57b9e7b..d50ef15cc2 100644 --- a/stochss/handlers/util/stochss_spatial_model.py +++ b/stochss/handlers/util/stochss_spatial_model.py @@ -84,6 +84,9 @@ class NewBC(BoundaryCondition): # pylint: disable=too-few-public-methods ######################################################################################## ''' __class__ = f"__main__.{boundary_condition['name']}" + def __init__(self): + pass + def expression(self): # pylint: disable=no-self-use ''' Custom expression for boundary condition