def test_loopless(self):
try:
solver = get_solver_name(mip=True)
except:
self.skip("no MILP solver found")
test_model = Model()
test_model.add_metabolites(Metabolite("A"))
test_model.add_metabolites(Metabolite("B"))
test_model.add_metabolites(Metabolite("C"))
EX_A = Reaction("EX_A")
EX_A.add_metabolites({test_model.metabolites.A: 1})
DM_C = Reaction("DM_C")
DM_C.add_metabolites({test_model.metabolites.C: -1})
v1 = Reaction("v1")
v1.add_metabolites({test_model.metabolites.A: -1,
test_model.metabolites.B: 1})
v2 = Reaction("v2")
v2.add_metabolites({test_model.metabolites.B: -1,
test_model.metabolites.C: 1})
v3 = Reaction("v3")
v3.add_metabolites({test_model.metabolites.C: -1,
test_model.metabolites.A: 1})
DM_C.objective_coefficient = 1
test_model.add_reactions([EX_A, DM_C, v1, v2, v3])
feasible_sol = construct_loopless_model(test_model).optimize()
v3.lower_bound = 1
infeasible_sol = construct_loopless_model(test_model).optimize()
self.assertEqual(feasible_sol.status, "optimal")
self.assertEqual(infeasible_sol.status, "infeasible")
def generate_fva_data(self, cobra_model, fraction_of_optimum=0.9,
objective_sense='maximize', reaction_list=None,
allow_loops=True, solver='glpk'):
print('FVA...')
#add in loop law constrain
if not allow_loops: cobra_model=construct_loopless_model(cobra_model);
# calculate the reaction bounds using FVA
self.fva_data = flux_variability_analysis(cobra_model, fraction_of_optimum=0.9,
objective_sense='maximize', solver=solver,
reaction_list=reaction_list,
);
def generate_fva_data(self, cobra_model, fraction_of_optimum=0.9,
objective_sense='maximize', reaction_list=None,
allow_loops=True, solver='glpk', verbose_I=True):
if verbose_I:
print('FVA...')
#add in loop law constrain
if not allow_loops: cobra_model=construct_loopless_model(cobra_model)
# calculate the reaction bounds using FVA
cobra_model.solver = solver
fva_data = flux_variability_analysis(cobra_model, fraction_of_optimum=0.9,
objective_sense='maximize',
reaction_list=reaction_list,
)
self.fva_data = dict(zip(list(fva_data.index),fva_data.to_dict('records')))
def generate_fba_data(self,cobra_model,allow_loops=True, method_I='fba',solver='glpk'):
"""perform FBA simulation on the model
"""
#add in loop law constrain
if not allow_loops: cobra_model=construct_loopless_model(cobra_model)
#check for the optimization method:
cobra_model.solver = solver
if method_I=='fba' or method_I=='loopless-fba':
sol = cobra_model.optimize()
elif method_I =='pfba' or method_I=='loopless-pfba':
sol = optimize_minimal_flux(model=cobra_model,solver=solver)
else:
print('method not recognized.')
return
self.fba_primal_data={}
for k,v in sol.x_dict.items():
self.fba_primal_data[k] = v
self.fba_dual_data={}
for k,v in sol.y_dict.items():
self.fba_dual_data[k] = v
model.metabolites.adp_c: -1,
model.metabolites.pi_c: -1,
model.metabolites.atp_c: 1,
model.metabolites.h2o_c: 1})
symporter1_reaction.lower_bound = -1000
symporter2_reaction.lower_bound = -1000
symporter3_reaction.lower_bound = -1000
atpase_reaction.lower_bound = -1000
model.add_reactions([symporter1_reaction, symporter2_reaction,
symporter3_reaction, atpase_reaction])
# Try to find the EGC that we just added to the model
sol = stFBA.find_egc(model)
print("Adding STFBA constraints...")
stfba_model = stFBA(model)
print("Calculating anaerobic yield with updated %s model" % model_name)
fba_sol = model.optimize(solver=settings.LP_SOLVER)
print("FBA max yield: %.3f" % fba_sol.f)
loopless_model = construct_loopless_model(model)
llfba_sol = loopless_model.optimize(solver=settings.LP_SOLVER)
print("ll-FBA max yield: %.3f" % llfba_sol.f)
stfba_sol = stfba_model.optimize()
if stfba_sol:
print("st-FBA max yield: %.3f" % stfba_sol.f)
DM_C = Reaction("DM_C")
DM_C.add_metabolites({test_model.metabolites.C: -1})
v1 = Reaction("v1")
v1.add_metabolites({test_model.metabolites.A: -1, test_model.metabolites.B: 1})
v2 = Reaction("v2")
v2.add_metabolites({test_model.metabolites.B: -1, test_model.metabolites.C: 1})
v3 = Reaction("v3")
v3.add_metabolites({test_model.metabolites.C: -1, test_model.metabolites.A: 1})
DM_C.objective_coefficient = 1
test_model.add_reactions([EX_A, DM_C, v1, v2, v3])
# While this model contains a loop, a flux state exists which has no flux
# through reaction v3, and is identified by loopless FBA.
construct_loopless_model(test_model).optimize()
# Output:
# <Solution 1000.00 at 0x62cd250>
# However, if flux is forced through v3, then there is no longer a feasible
# loopless solution.
v3.lower_bound = 1
construct_loopless_model(test_model).optimize()
# Output:
# <Solution 'infeasible' at 0x62cd5d0>
# Loopless FBA is also possible on genome scale models, but it requires a
# capable MILP solver.
salmonella = cobra.test.create_test_model()
EX_A.add_metabolites({test_model.metabolites.A: 1})
DM_C = Reaction("DM_C")
DM_C.add_metabolites({test_model.metabolites.C: -1})
v1 = Reaction("v1")
v1.add_metabolites({test_model.metabolites.A: -1, test_model.metabolites.B: 1})
v2 = Reaction("v2")
v2.add_metabolites({test_model.metabolites.B: -1, test_model.metabolites.C: 1})
v3 = Reaction("v3")
v3.add_metabolites({test_model.metabolites.C: -1, test_model.metabolites.A: 1})
DM_C.objective_coefficient = 1
test_model.add_reactions([EX_A, DM_C, v1, v2, v3])
# While this model contains a loop, a flux state exists which has no flux
# through reaction v3, and is identified by loopless FBA.
construct_loopless_model(test_model).optimize()
# Output:
# <Solution 1000.00 at 0x62cd250>
# However, if flux is forced through v3, then there is no longer a feasible
# loopless solution.
v3.lower_bound = 1
construct_loopless_model(test_model).optimize()
# Output:
# <Solution 'infeasible' at 0x62cd5d0>
# Loopless FBA is also possible on genome scale models, but it requires a
# capable MILP solver.
salmonella = cobra.test.create_test_model()
