def test_homeostatic_singleAboveObjective(self):
fba = FluxBalanceAnalysis(**_testTargetMolecules)
externalMoleculeLevels = {"A": 50, "D": 20}
fba.setExternalMoleculeLevels([
externalMoleculeLevels[moleculeID]
for moleculeID in fba.getExternalMoleculeIDs()
])
internalMoleculeLevels = {
"B": 15,
"C": 10,
"E": 20,
}
fba.setInternalMoleculeLevels([
internalMoleculeLevels[moleculeID]
for moleculeID in fba.getInternalMoleculeIDs()
])
for moleculeID, change in zip(fba.getOutputMoleculeIDs(),
fba.getOutputMoleculeLevelsChange()):
if moleculeID == "B":
self.assertAlmostEqual(-5, change)
elif moleculeID == "C":
self.assertAlmostEqual(0, change)
elif moleculeID == "E":
self.assertAlmostEqual(0, change)
def checkErrors(targetFluxes,
fixedReactionNames=["v_biomass"],
reactionStoichiometry=toyModelReactionStoichWithBiomass,
transportLimits=transportLimits):
fba_moma = FluxBalanceAnalysis(
reactionStoich=reactionStoichiometry,
externalExchangedMolecules=transportLimits.keys(),
objective=targetFluxes,
objectiveType="moma",
objectiveParameters={"fixedReactionNames": fixedReactionNames},
solver="glpk",
)
exchangeMolecules = fba_moma.getExternalMoleculeIDs()
fba_moma.setExternalMoleculeLevels(
[transportLimits[molID] for molID in exchangeMolecules])
return fba_moma.errorFluxes(), fba_moma.errorAdjustedReactionFluxes()
def testModel(toyModelReactionStoich=toyModelReactionStoich, biomassReactionStoich=biomassReactionStoich, transportLimits=transportLimits, reactionEnzymes=reactionEnzymes, enzymeConcentrations=enzymeConcentrations): fba = FluxBalanceAnalysis( reactionStoich=toyModelReactionStoich, externalExchangedMolecules=transportLimits.keys(), objective=biomassReactionStoich["v_biomass"], objectiveType="standard", solver="glpk", ) exchangeMolecules = fba.getExternalMoleculeIDs() fba.setExternalMoleculeLevels([transportLimits[molID] for molID in exchangeMolecules]) for reactionID in toyModelReactionStoich: if reactionID in reactionEnzymes: enzymeID = reactionEnzymes[reactionID] if enzymeID in enzymeConcentrations: fba.setMaxReactionFlux(reactionID, KCAT_MAX * enzymeConcentrations[enzymeID]) return fba.getBiomassReactionFlux()[0]
def test_standard(self):
fba = FluxBalanceAnalysis(**_testStandard)
externalMoleculeLevels = {"A": 50, "D": 20}
fba.setExternalMoleculeLevels([
externalMoleculeLevels[moleculeID]
for moleculeID in fba.getExternalMoleculeIDs()
])
self.assertEqual(fba.getBiomassReactionFlux(), 1.0)
for moleculeID, change in zip(fba.getOutputMoleculeIDs(),
fba.getOutputMoleculeLevelsChange()):
if moleculeID == "B":
self.assertAlmostEqual(10, change)
elif moleculeID == "C":
self.assertAlmostEqual(10, change)
elif moleculeID == "E":
self.assertAlmostEqual(20, change)
"ATP": 1
},
}
biomassReactionStoich = {"v_biomass": {"C": 1, "F": 1, "H": 1, "ATP": 10}}
transportLimits = {
"A": 21.0,
"F": 5.0,
"D": -12.0,
"E": -12.0,
"H": 5.0,
"O2": 15.0,
}
fba = FluxBalanceAnalysis(
reactionStoich=toyModelReactionStoich,
externalExchangedMolecules=transportLimits.keys(),
objective=biomassReactionStoich["v_biomass"],
objectiveType="standard",
solver="glpk",
)
exchangeMolecules = fba.getExternalMoleculeIDs()
fba.setExternalMoleculeLevels(
[transportLimits[molID] for molID in exchangeMolecules])
biomassReactionFlux = fba.getBiomassReactionFlux()[0]
print biomassReactionFlux
for reactionIndex, reaction in enumerate(reactionIDs):
# Set upper bound constraint according to transport rates
if reaction in transportRates:
maxReactionFluxes[reactionIndex] = transportRates[reaction]
fba.setMinReactionFluxes(reactionIDs, np.zeros(nReactions))
fba.setMaxReactionFluxes(reactionIDs, maxReactionFluxes)
# Describe external molecule levels
moleculeIDs = fba.getInternalMoleculeIDs()
nMolecules = len(moleculeIDs)
externalMoleculeLevels = np.inf * np.ones(nExternalExchangedMolecules)
fba.setExternalMoleculeLevels(externalMoleculeLevels)
# Describe internal molecule levels
moleculeCountsInit = np.zeros(nMolecules)
internalMoleculeLevels = moleculeCountsInit
# Perform simulation
nTimesteps = 15
moleculeCounts = []
moleculeCounts.append(internalMoleculeLevels)
foods = ["ATP"]
foodUnit = 2
for timestep in np.arange(nTimesteps - 1):
deltaMolecules = runFBA(internalMoleculeLevels)
"O2": 10,
"NADH": 25,
"ATP": 50
}
metaboliteConcentrations = [objective[x] for x in sorted(objective.keys())]
step = 1
while step <= 12:
print "\nStep %i" % (step)
fba = FluxBalanceAnalysis(reactions,
externalMolecules,
objective,
objectiveType="homeostatic",
solver="glpk")
fba.setInternalMoleculeLevels(metaboliteConcentrations)
fba.setExternalMoleculeLevels(
[externalMolecules[x] for x in externalMolecules])
metaboliteConcentrations += fba.getOutputMoleculeLevelsChange()
print "Metabolite changes:\n%s" % (fba.getOutputMoleculeLevelsChange())
print "Metabolite conc:\n%s" % (metaboliteConcentrations)
# print "Objective value: %f" % (fba.objectiveValue())
# consume some metabolites
metaboliteConcentrations *= 0.5
# consume all of ATP
# metaboliteConcentrations[1] = 0
step += 1