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PyCoExp

A python package that allows users to integrate expression data into Copasi (.cps) models. Additionally, it allows users to use copasi model and perform the following tasks: steadystate calculation, metabolic control analysis, and parameter scan.

The pycoexp framework is developed using Python 3.

Cloning and installing pycoexp package

 $ pip install git+https://github.com/surajsept/pycoexp.git

Once you have checked the steps above. You may consider refering to the Beginners Guide. Or, just start a python console to:

Integrate expression values into a copasi model.

import pycoexp.tasks
task = pycoexp.tasks.tasks()
# integrate expression values and save updated copasi models in folder 'updatedModels'
task.integrate_expression(filepath_CPSmodel='model.cps', filepath_expdata='ExpData.csv', 
                          filepath_mapping='mapping.csv', foldername='updatedModels/')

File templates:

Mapping file template

Name Model_ID ...

Expression file template

Name Exp1 Exp2 ...

here, "Name" corresponds to the gene name/symbol, "Model_ID" stands for the ID in SBML model and "Exp1..." stand for experiment/observation name.

Additionally,

Example 1: To calculate steady state

# get steady state
Concentration, Flux = task.steadystate(filepath_CPSmodel='model.cps')

Example 2: To calculate control coefficients

# perform metabolic control analysis
ControlCoefficients = task.mca(filepath_CPSmodel='model.cps', system_variable='concentration', verbose=True)

Example 3: To do a parameter scan

# do parameter scan
Conc, Fluxes = task.scan(filepath_CPSmodel='model.cps', parameter_name='NAMPT', E_T_or_k1='E_T', lb=0.1, ub=1.0, n=10, rescaling=True)

Example 4: To do a time-course simulation

# do time-course simulation
Conc, Fluxes = task.time_course(filepath_CPSmodel='model.cps', duration=100, stepsize=0.1)

This is a development version, hosted as a private git repository, and people interested in contributing can request access by contacting Suraj Sharma ([email protected]).