Feat: integration layer with multitfa

docs/03-thermodynamics.rst

@@ -273,7 +273,7 @@ or the thermodynamic constraint, see the
     )
     # constrain the model objective so that the feashibility relaxation recovers growth
     tmodel_prot.reactions.BIOMASS_Ecoli_core_w_GAM.lower_bound = solution.objective_value
-    iis, status = geckopy.integration.relax_thermo_proteins(
+    iis, status = geckopy.integration.relaxation.relax_thermo_proteins(
         tmodel_prot, 
         prot_candidates=[prot.id for prot in tmodel_prot.proteins], 
         objective_rule=geckopy.experimental.relaxation.Objective_rule.MIN_ELASTIC_SUM

geckopy/integration/__init__.py

@@ -11,4 +11,3 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from .relaxation import relax_thermo_concentrations_proteins, relax_thermo_proteins

geckopy/integration/multitfa.py

@@ -0,0 +1,123 @@
+from copy import copy, deepcopy
+
+from cobra.core.dictlist import DictList
+from multitfa.core import Thermo_met, thermo_reaction, tmodel
+
+import geckopy
+
+
+class ThermoProtReaction(thermo_reaction):
+    """
+    Class representation of thermo reaction Object. We calculate the required thermodynamic constraints for performing tMFA. To do the constraints, we need Gibbs energy of reaction and transport.
+
+    Parameters
+    ----------
+    cobra_rxn : cobra.core.Reaction
+        Cobra reaction object, to copy the attributes from. We copy metabolites and genes.
+    updated_model : core.tmodel, optional
+        tmodel object, with updated thermo properties, by default None
+
+    """
+
+    def __init__(
+        self,
+        cobra_rxn,
+        updated_model=None,
+    ):
+        self._model = updated_model
+        do_not_copy_by_ref = {"_model", "_metabolites", "_genes"}
+        for attr, value in cobra_rxn.__dict__.items():
+            if attr not in do_not_copy_by_ref:
+                self.__dict__[attr] = copy(value)
+
+        self._metabolites = {}
+        for met, stoic in cobra_rxn._metabolites.items():
+            # this is the only change; also account for proteins
+            new_met = (
+                self.model.metabolites.get_by_id(met.id)
+                if met in self.model.metabolites
+                else self.model.proteins.get_by_id(met.id)
+            )
+            self._metabolites[new_met] = stoic
+            new_met._reaction.add(self)
+        self._genes = set()
+        for gene in cobra_rxn._genes:
+            new_gene = self.model.genes.get_by_id(gene.id)
+            self._genes.add(new_gene)
+            new_gene._reaction.add(self)
+
+
+class ThermoProtModel(tmodel):
+    def __init__(
+        self,
+        model,
+        Exclude_list=[],
+        tolerance_integral=1e-9,
+        compartment_info=None,
+        membrane_potential=None,
+        exclude_metabolites=[],
+    ):
+
+        self.compartment_info = compartment_info
+        self.membrane_potential = membrane_potential
+
+        do_not_copy_by_ref = {
+            "metabolites",
+            "reactions",
+            "proteins",
+            "genes",
+            "notes",
+            "annotation",
+        }
+        for attr in model.__dict__:
+            if attr not in do_not_copy_by_ref:
+                self.__dict__[attr] = model.__dict__[attr]
+
+        self.metabolites = DictList()
+        do_not_copy_by_ref = {"_reaction", "_model"}
+        for metabolite in model.metabolites:
+            new_met = Thermo_met(
+                metabolite=metabolite,
+                updated_model=self,
+            )
+            self.metabolites.append(new_met)
+
+        self.genes = DictList()
+
+        for gene in model.genes:
+            new_gene = gene.__class__(None)
+            for attr, value in gene.__dict__.items():
+                if attr not in do_not_copy_by_ref:
+                    new_gene.__dict__[attr] = (
+                        copy(value) if attr == "formula" else value
+                    )
+            new_gene._model = self
+            self.genes.append(new_gene)
+        self.proteins = DictList()
+        for protein in model.proteins:
+            new_prot = Thermo_met(
+                metabolite=protein,
+                updated_model=self,
+            )
+            # proteins do not participate in dGf calculations
+            self.proteins.append(new_prot)
+
+        self.reactions = DictList()
+        do_not_copy_by_ref = {"_model", "_metabolites", "_genes"}
+        for reaction in model.reactions:
+            # this is custom to make the reaction aware of proteins
+            new_reaction = ThermoProtReaction(
+                cobra_rxn=reaction,
+                updated_model=self,
+            )
+            self.reactions.append(new_reaction)
+
+        try:
+            self._solver = deepcopy(model.solver)
+            # Cplex has an issue with deep copies
+        except Exception:  # pragma: no cover
+            self._solver = copy(model.solver)  # pragma: no cover
+
+        self.Exclude_list = Exclude_list
+        self.solver.configuration.tolerances.integrality = tolerance_integral
+        self._var_update = False

tests/test_integration/test_multitfa.py

@@ -0,0 +1,18 @@
+import pytest
+from optlang import available_solvers
+
+from geckopy.integration.multitfa import ThermoProtModel
+
+
+@pytest.mark.skipif(
+    not (available_solvers["GUROBI"] or available_solvers["CPLEX"]),
+    reason="No quadratic programming available",
+)
+def test_protein_constrain_affects_multitfa_solution(ec_model_core):
+    """Check thermo model returns different solution when protein is constrained."""
+    thermo_model = ThermoProtModel(ec_model_core.copy())
+    tsol = thermo_model.slim_optimize()
+    ec_model_core.proteins.prot_P25516.add_concentration(2e-5)
+    thermo_model = ThermoProtModel(ec_model_core)
+    tsol_prot = thermo_model.slim_optimize()
+    assert pytest.approx(tsol) != tsol_prot

tests/test_integration/test_relaxation.py

@@ -23,7 +23,7 @@
 
 from geckopy.experimental import from_copy_number
 from geckopy.experimental.relaxation import Objective_rule
-from geckopy.integration import (
+from geckopy.integration.relaxation import (
     relax_thermo_concentrations_proteins,
     relax_thermo_proteins,
 )