def test_solve_mip(self, solver_test):
solver, old_solution, infeasible_model = solver_test
if not hasattr(solver, "_SUPPORTS_MILP") or not solver._SUPPORTS_MILP:
pytest.skip("no milp support")
cobra_model = Model('MILP_implementation_test')
constraint = Metabolite("constraint")
constraint._bound = 2.5
x = Reaction("x")
x.lower_bound = 0.
x.objective_coefficient = 1.
x.add_metabolites({constraint: 2.5})
y = Reaction("y")
y.lower_bound = 0.
y.objective_coefficient = 1.
y.add_metabolites({constraint: 1.})
cobra_model.add_reactions([x, y])
float_sol = solver.solve(cobra_model)
# add an integer constraint
y.variable_kind = "integer"
int_sol = solver.solve(cobra_model)
assert abs(float_sol.f - 2.5) < 10 ** -5
assert abs(float_sol.x_dict["y"] - 2.5) < 10 ** -5
assert int_sol.status == "optimal"
assert abs(int_sol.f - 2.2) < 10 ** -3
assert abs(int_sol.x_dict["y"] - 2.0) < 10 ** -3
def construct_loopless_model(cobra_model):
"""Construct a loopless model.
This adds MILP constraints to prevent flux from proceeding in a loop, as
done in http://dx.doi.org/10.1016/j.bpj.2010.12.3707
Please see the documentation for an explanation of the algorithm.
This must be solved with an MILP capable solver.
"""
# copy the model and make it irreversible
model = cobra_model.copy()
convert_to_irreversible(model)
max_ub = max(model.reactions.list_attr("upper_bound"))
# a dict for storing S^T
thermo_stoic = {
"thermo_var_" + metabolite.id: {}
for metabolite in model.metabolites
}
# Slice operator is so that we don't get newly added metabolites
original_metabolites = model.metabolites[:]
for reaction in model.reactions[:]:
# Boundary reactions are not subjected to these constraints
if len(reaction._metabolites) == 1:
continue
# populate the S^T dict
bound_id = "thermo_bound_" + reaction.id
for met, stoic in iteritems(reaction._metabolites):
thermo_stoic["thermo_var_" + met.id][bound_id] = stoic
# I * 1000 > v --> I * 1000 - v > 0
reaction_ind = Reaction(reaction.id + "_indicator")
reaction_ind.variable_kind = "integer"
reaction_ind.upper_bound = 1
reaction_ub = Metabolite(reaction.id + "_ind_ub")
reaction_ub._constraint_sense = "G"
reaction.add_metabolites({reaction_ub: -1})
reaction_ind.add_metabolites({reaction_ub: max_ub})
# This adds a compensating term for 0 flux reactions, so we get
# S^T x - (1 - I) * 1001 < -1 which becomes
# S^T x < 1000 for 0 flux reactions and
# S^T x < -1 for reactions with nonzero flux.
reaction_bound = Metabolite(bound_id)
reaction_bound._constraint_sense = "L"
reaction_bound._bound = max_ub
reaction_ind.add_metabolites({reaction_bound: max_ub + 1})
model.add_reaction(reaction_ind)
for metabolite in original_metabolites:
metabolite_var = Reaction("thermo_var_" + metabolite.id)
metabolite_var.lower_bound = -max_ub
model.add_reaction(metabolite_var)
metabolite_var.add_metabolites({
model.metabolites.get_by_id(k): v
for k, v in iteritems(thermo_stoic[metabolite_var.id])
})
return model
def construct_loopless_model(cobra_model):
"""Construct a loopless model.
This adds MILP constraints to prevent flux from proceeding in a loop, as
done in http://dx.doi.org/10.1016/j.bpj.2010.12.3707
Please see the documentation for an explanation of the algorithm.
This must be solved with an MILP capable solver.
"""
# copy the model and make it irreversible
model = cobra_model.copy()
convert_to_irreversible(model)
max_ub = max(model.reactions.list_attr("upper_bound"))
# a dict for storing S^T
thermo_stoic = {"thermo_var_" + metabolite.id: {}
for metabolite in model.metabolites}
# Slice operator is so that we don't get newly added metabolites
original_metabolites = model.metabolites[:]
for reaction in model.reactions[:]:
# Boundary reactions are not subjected to these constraints
if len(reaction._metabolites) == 1:
continue
# populate the S^T dict
bound_id = "thermo_bound_" + reaction.id
for met, stoic in iteritems(reaction._metabolites):
thermo_stoic["thermo_var_" + met.id][bound_id] = stoic
# I * 1000 > v --> I * 1000 - v > 0
reaction_ind = Reaction(reaction.id + "_indicator")
reaction_ind.variable_kind = "integer"
reaction_ind.upper_bound = 1
reaction_ub = Metabolite(reaction.id + "_ind_ub")
reaction_ub._constraint_sense = "G"
reaction.add_metabolites({reaction_ub: -1})
reaction_ind.add_metabolites({reaction_ub: max_ub})
# This adds a compensating term for 0 flux reactions, so we get
# S^T x - (1 - I) * 1001 < -1 which becomes
# S^T x < 1000 for 0 flux reactions and
# S^T x < -1 for reactions with nonzero flux.
reaction_bound = Metabolite(bound_id)
reaction_bound._constraint_sense = "L"
reaction_bound._bound = max_ub
reaction_ind.add_metabolites({reaction_bound: max_ub + 1})
model.add_reaction(reaction_ind)
for metabolite in original_metabolites:
metabolite_var = Reaction("thermo_var_" + metabolite.id)
metabolite_var.lower_bound = -max_ub
model.add_reaction(metabolite_var)
metabolite_var.add_metabolites(
{model.metabolites.get_by_id(k): v
for k, v in iteritems(thermo_stoic[metabolite_var.id])})
return model
