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 test_standard_noInput(self):
fba = FluxBalanceAnalysis(**_testStandard)
self.assertEqual(fba.getBiomassReactionFlux(), 0)
for moleculeID, change in zip(fba.getOutputMoleculeIDs(),
fba.getOutputMoleculeLevelsChange()):
if moleculeID == "B":
self.assertAlmostEqual(0, change)
elif moleculeID == "C":
self.assertAlmostEqual(0, change)
elif moleculeID == "E":
self.assertAlmostEqual(0, change)
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 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 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
# - externalExchangedMolecules, an iterable of strings (moleculeIDs). Every provided ID will be set up with an exchange flux.
externalExchangedMolecules = []
for reaction in transportFluxes:
for molecule in reaction["stoichiometry"].keys():
externalExchangedMolecules.append(molecule)
nExternalExchangedMolecules = len(externalExchangedMolecules)
# Describe the objective
# - objective, a dict of strings:floats (moleculeID:objective value). The meaning and usage of the objective will vary depending on the formulation of FBA desired.
objective = FBAObjectives
# Set up FBA solver
fba = FluxBalanceAnalysis(
reactionStoich,
externalExchangedMolecules,
objective,
objectiveType="homeostatic",
)
# Describe reaction flux constraints
reactionIDs = reactionStoich.keys()
nReactions = len(reactionIDs)
maxReactionFluxes = np.inf * np.ones(nReactions)
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))
def flux_sensitivity(self, metaboliteConcentrations,
externalMoleculeLevels, catalyzedReactionBounds,
targets, coefficient):
"""
Sensitivity performed with flux_sensitivity variant simulations. Disables
kinetic constraints one by one to determine impact on certain fluxes.
Args:
metaboliteConcentrations (ndarray[float] with mol/volume units):
concentrations for all metabolites
externalMoleculeLevels (ndarray[float]): limits for external
molecule exchanges
catalyzedReactionBounds (ndarray[float]): max limit for each
enzyme catalyzed reaction
targets (ndarray[float]): flux target for each reaction with a
kinetic constraint
coefficient (float with mass-time/volume units): conversion factor
for fluxes
"""
succ_rxn = 'SUCCINATE-DEHYDROGENASE-UBIQUINONE-RXN-SUC/UBIQUINONE-8//FUM/CPD-9956.31.'
iso_rxn = 'ISOCITDEH-RXN'
succ_flux = []
iso_flux = []
for rxn in self.kineticsConstrainedReactions:
fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
fba.enableKineticTargets()
fba.setInternalMoleculeLevels(
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self._setExternalMoleculeLevels(fba, externalMoleculeLevels,
metaboliteConcentrations)
flux = (self.ngam * coefficient).asNumber(COUNTS_UNITS /
VOLUME_UNITS)
fba.setReactionFluxBounds(fba._reactionID_NGAM,
lowerBounds=flux,
upperBounds=flux)
flux = self.currentPolypeptideElongationEnergy.asNumber(
COUNTS_UNITS / VOLUME_UNITS)
fba.setReactionFluxBounds(
self.fba._reactionID_polypeptideElongationEnergy,
lowerBounds=flux,
upperBounds=flux)
fba.setReactionFluxBounds(self.reactionsWithCatalystsList,
upperBounds=catalyzedReactionBounds,
raiseForReversible=False)
fba.setKineticTarget(self.kineticsConstrainedReactions,
targets,
raiseForReversible=False)
fba.disableKineticTargets(rxn)
# Fluxes of interest for each disabled constraint
succ_flux += [
((COUNTS_UNITS / VOLUME_UNITS) *
fba.getReactionFluxes(succ_rxn)[0] / coefficient).asNumber(
units.mmol / units.g / units.h)
]
iso_flux += [
((COUNTS_UNITS / VOLUME_UNITS) *
fba.getReactionFluxes(iso_rxn)[0] / coefficient).asNumber(
units.mmol / units.g / units.h)
]
# Fluxes of interest for the original simulation
succ_flux += [((COUNTS_UNITS / VOLUME_UNITS) *
self.fba.getReactionFluxes(succ_rxn)[0] /
coefficient).asNumber(units.mmol / units.g / units.h)]
iso_flux += [((COUNTS_UNITS / VOLUME_UNITS) *
self.fba.getReactionFluxes(iso_rxn)[0] /
coefficient).asNumber(units.mmol / units.g / units.h)]
self.writeToListener('FBAResults', 'succinate_flux_sensitivity',
np.array(succ_flux))
self.writeToListener('FBAResults', 'isocitrate_flux_sensitivity',
np.array(iso_flux))
def initialize(self, sim, sim_data):
super(Metabolism, self).initialize(sim, sim_data)
# Load constants
self.nAvogadro = sim_data.constants.nAvogadro
self.cellDensity = sim_data.constants.cellDensity
self.ngam = sim_data.constants.nonGrowthAssociatedMaintenance
self.exchangeConstraints = sim_data.process.metabolism.exchangeConstraints
self.getBiomassAsConcentrations = sim_data.mass.getBiomassAsConcentrations
self.nutrientToDoublingTime = sim_data.nutrientToDoublingTime
# Create objective for homeostatic constraints
nutrients_time_series_label = sim_data.external_state.environment.nutrients_time_series_label
concDict = sim_data.process.metabolism.concentrationUpdates.concentrationsBasedOnNutrients(
sim_data.external_state.environment.
nutrients_time_series[nutrients_time_series_label][0][1])
self.concModificationsBasedOnCondition = self.getBiomassAsConcentrations(
sim_data.conditionToDoublingTime[sim_data.condition])
concDict.update(self.concModificationsBasedOnCondition)
self.homeostaticObjective = dict(
(key, concDict[key].asNumber(COUNTS_UNITS / VOLUME_UNITS))
for key in concDict)
# Load initial mass
initWaterMass = sim_data.mass.avgCellWaterMassInit
initDryMass = sim_data.mass.avgCellDryMassInit
initCellMass = initWaterMass + initDryMass
energyCostPerWetMass = sim_data.constants.darkATP * initDryMass / initCellMass
# Setup molecules in external environment that can be exchanged
externalExchangedMolecules = sim_data.external_state.environment.nutrient_data[
"secretionExchangeMolecules"]
self.metaboliteNamesFromNutrients = set()
for time, nutrientsLabel in sim_data.external_state.environment.nutrients_time_series[
nutrients_time_series_label]:
externalExchangedMolecules += sim_data.external_state.environment.nutrient_data[
"importExchangeMolecules"][nutrientsLabel]
self.metaboliteNamesFromNutrients.update(
sim_data.process.metabolism.concentrationUpdates.
concentrationsBasedOnNutrients(
nutrientsLabel,
sim_data.process.metabolism.nutrientsToInternalConc))
externalExchangedMolecules = sorted(set(externalExchangedMolecules))
self.metaboliteNamesFromNutrients = sorted(
self.metaboliteNamesFromNutrients)
moleculeMasses = dict(
zip(
externalExchangedMolecules,
sim_data.getter.getMass(externalExchangedMolecules).asNumber(
MASS_UNITS / COUNTS_UNITS)))
# Data structures to compute reaction bounds based on enzyme presence/absence
self.catalystsList = sim_data.process.metabolism.catalystsList
self.reactionsWithCatalystsList = sim_data.process.metabolism.reactionCatalystsList
self.reactionCatalystsDict = sim_data.process.metabolism.reactionCatalysts
catalysisMatrixI = sim_data.process.metabolism.catalysisMatrixI
catalysisMatrixJ = sim_data.process.metabolism.catalysisMatrixJ
catalysisMatrixV = sim_data.process.metabolism.catalysisMatrixV
shape = (catalysisMatrixI.max() + 1, catalysisMatrixJ.max() + 1)
self.catalysisMatrix = csr_matrix(
(catalysisMatrixV, (catalysisMatrixI, catalysisMatrixJ)),
shape=shape)
self.catalyzedReactionBoundsPrev = np.inf * np.ones(
len(self.reactionsWithCatalystsList))
# Function to compute reaction targets based on kinetic parameters and molecule concentrations
self.getKineticConstraints = sim_data.process.metabolism.getKineticConstraints
# Remove disabled reactions so they don't get included in the FBA problem setup
if hasattr(
sim_data.process.metabolism, "kineticTargetShuffleRxns"
) and sim_data.process.metabolism.kineticTargetShuffleRxns is not None:
self.kineticsConstrainedReactions = sim_data.process.metabolism.kineticTargetShuffleRxns
self.active_constraints_mask = np.ones(len(
self.kineticsConstrainedReactions),
dtype=bool)
else:
constrainedReactionList = sim_data.process.metabolism.constrainedReactionList
constraintsToDisable = sim_data.process.metabolism.constraintsToDisable
self.active_constraints_mask = np.array([
(rxn not in constraintsToDisable)
for rxn in constrainedReactionList
])
self.kineticsConstrainedReactions = list(
np.array(constrainedReactionList)[
self.active_constraints_mask])
self.kineticsEnzymesList = sim_data.process.metabolism.enzymeIdList
self.kineticsSubstratesList = sim_data.process.metabolism.kineticsSubstratesList
constraintToReactionMatrixI = sim_data.process.metabolism.constraintToReactionMatrixI
constraintToReactionMatrixJ = sim_data.process.metabolism.constraintToReactionMatrixJ
constraintToReactionMatrixV = sim_data.process.metabolism.constraintToReactionMatrixV
shape = (constraintToReactionMatrixI.max() + 1,
constraintToReactionMatrixJ.max() + 1)
self.constraintToReactionMatrix = np.zeros(shape, np.float64)
self.constraintToReactionMatrix[
constraintToReactionMatrixI,
constraintToReactionMatrixJ] = constraintToReactionMatrixV
self.constraintIsKcatOnly = sim_data.process.metabolism.constraintIsKcatOnly
# Set solver and kinetic objective weight (lambda)
solver = sim_data.process.metabolism.solver
kinetic_objective_weight = sim_data.process.metabolism.kinetic_objective_weight
# Disable kinetics completely if weight is 0 or specified in file above
self.use_kinetics = True
if not USE_KINETICS or kinetic_objective_weight == 0:
self.use_kinetics = False
kinetic_objective_weight = 0
# Set up FBA solver
# reactionRateTargets value is just for initialization, it gets reset each timestep during evolveState
self.fbaObjectOptions = {
"reactionStoich":
sim_data.process.metabolism.reactionStoich,
"externalExchangedMolecules":
externalExchangedMolecules,
"objective":
self.homeostaticObjective,
"objectiveType":
"homeostatic_kinetics_mixed",
"objectiveParameters": {
"kineticObjectiveWeight": kinetic_objective_weight,
"reactionRateTargets": {
reaction: 1
for reaction in self.kineticsConstrainedReactions
},
"oneSidedReactionTargets": [],
},
"moleculeMasses":
moleculeMasses,
"secretionPenaltyCoeff":
sim_data.constants.
secretion_penalty_coeff, # The "inconvenient constant"--limit secretion (e.g., of CO2)
"solver":
solver,
"maintenanceCostGAM":
energyCostPerWetMass.asNumber(COUNTS_UNITS / MASS_UNITS),
"maintenanceReaction":
sim_data.process.metabolism.maintenanceReaction,
}
if not self.use_kinetics:
self.fbaObjectOptions["objectiveType"] = "homeostatic"
self.fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
self.internalExchangeIdxs = np.array([
self.metaboliteNamesFromNutrients.index(x)
for x in self.fba.getOutputMoleculeIDs()
])
# Disable all rates during burn-in
if self.use_kinetics:
if KINETICS_BURN_IN_PERIOD > 0:
self.fba.disableKineticTargets()
self.burnInComplete = False
else:
self.burnInComplete = True
# Values will get updated at each time point
self.currentNgam = 1 * (COUNTS_UNITS / VOLUME_UNITS)
self.currentPolypeptideElongationEnergy = 1 * (COUNTS_UNITS /
VOLUME_UNITS)
# External molecules
self.externalMoleculeIDs = self.fba.getExternalMoleculeIDs()
# Views
self.metaboliteNames = self.fba.getOutputMoleculeIDs()
self.metabolites = self.bulkMoleculesView(
self.metaboliteNamesFromNutrients)
self.catalysts = self.bulkMoleculesView(self.catalystsList)
self.kineticsEnzymes = self.bulkMoleculesView(self.kineticsEnzymesList)
self.kineticsSubstrates = self.bulkMoleculesView(
self.kineticsSubstratesList)
outputMoleculeIDs = self.fba.getOutputMoleculeIDs()
assert outputMoleculeIDs == self.fba.getInternalMoleculeIDs()
# Set the priority to a low value
self.bulkMoleculesRequestPriorityIs(REQUEST_PRIORITY_METABOLISM)
self.AAs = [
x[:-3] for x in sorted(sim_data.amino_acid_1_to_3_ordered.values())
]
self.shuffleIdxs = None
if hasattr(
sim_data.process.metabolism, "kineticTargetShuffleIdxs"
) and sim_data.process.metabolism.kineticTargetShuffleIdxs is not None:
self.shuffleIdxs = sim_data.process.metabolism.kineticTargetShuffleIdxs
self.shuffleCatalyzedIdxs = None
if hasattr(
sim_data.process.metabolism, "catalystShuffleIdxs"
) and sim_data.process.metabolism.catalystShuffleIdxs is not None:
self.shuffleCatalyzedIdxs = sim_data.process.metabolism.catalystShuffleIdxs
self.run_flux_sensitivity = getattr(sim_data.process.metabolism,
'run_flux_sensitivity', False)
class Metabolism(wholecell.processes.process.Process):
""" Metabolism """
_name = "Metabolism"
# Constructor
def __init__(self):
super(Metabolism, self).__init__()
# Construct object graph
def initialize(self, sim, sim_data):
super(Metabolism, self).initialize(sim, sim_data)
# Load constants
self.nAvogadro = sim_data.constants.nAvogadro
self.cellDensity = sim_data.constants.cellDensity
self.ngam = sim_data.constants.nonGrowthAssociatedMaintenance
self.exchangeConstraints = sim_data.process.metabolism.exchangeConstraints
self.getBiomassAsConcentrations = sim_data.mass.getBiomassAsConcentrations
self.nutrientToDoublingTime = sim_data.nutrientToDoublingTime
# Create objective for homeostatic constraints
nutrients_time_series_label = sim_data.external_state.environment.nutrients_time_series_label
concDict = sim_data.process.metabolism.concentrationUpdates.concentrationsBasedOnNutrients(
sim_data.external_state.environment.
nutrients_time_series[nutrients_time_series_label][0][1])
self.concModificationsBasedOnCondition = self.getBiomassAsConcentrations(
sim_data.conditionToDoublingTime[sim_data.condition])
concDict.update(self.concModificationsBasedOnCondition)
self.homeostaticObjective = dict(
(key, concDict[key].asNumber(COUNTS_UNITS / VOLUME_UNITS))
for key in concDict)
# Load initial mass
initWaterMass = sim_data.mass.avgCellWaterMassInit
initDryMass = sim_data.mass.avgCellDryMassInit
initCellMass = initWaterMass + initDryMass
energyCostPerWetMass = sim_data.constants.darkATP * initDryMass / initCellMass
# Setup molecules in external environment that can be exchanged
externalExchangedMolecules = sim_data.external_state.environment.nutrient_data[
"secretionExchangeMolecules"]
self.metaboliteNamesFromNutrients = set()
for time, nutrientsLabel in sim_data.external_state.environment.nutrients_time_series[
nutrients_time_series_label]:
externalExchangedMolecules += sim_data.external_state.environment.nutrient_data[
"importExchangeMolecules"][nutrientsLabel]
self.metaboliteNamesFromNutrients.update(
sim_data.process.metabolism.concentrationUpdates.
concentrationsBasedOnNutrients(
nutrientsLabel,
sim_data.process.metabolism.nutrientsToInternalConc))
externalExchangedMolecules = sorted(set(externalExchangedMolecules))
self.metaboliteNamesFromNutrients = sorted(
self.metaboliteNamesFromNutrients)
moleculeMasses = dict(
zip(
externalExchangedMolecules,
sim_data.getter.getMass(externalExchangedMolecules).asNumber(
MASS_UNITS / COUNTS_UNITS)))
# Data structures to compute reaction bounds based on enzyme presence/absence
self.catalystsList = sim_data.process.metabolism.catalystsList
self.reactionsWithCatalystsList = sim_data.process.metabolism.reactionCatalystsList
self.reactionCatalystsDict = sim_data.process.metabolism.reactionCatalysts
catalysisMatrixI = sim_data.process.metabolism.catalysisMatrixI
catalysisMatrixJ = sim_data.process.metabolism.catalysisMatrixJ
catalysisMatrixV = sim_data.process.metabolism.catalysisMatrixV
shape = (catalysisMatrixI.max() + 1, catalysisMatrixJ.max() + 1)
self.catalysisMatrix = csr_matrix(
(catalysisMatrixV, (catalysisMatrixI, catalysisMatrixJ)),
shape=shape)
self.catalyzedReactionBoundsPrev = np.inf * np.ones(
len(self.reactionsWithCatalystsList))
# Function to compute reaction targets based on kinetic parameters and molecule concentrations
self.getKineticConstraints = sim_data.process.metabolism.getKineticConstraints
# Remove disabled reactions so they don't get included in the FBA problem setup
if hasattr(
sim_data.process.metabolism, "kineticTargetShuffleRxns"
) and sim_data.process.metabolism.kineticTargetShuffleRxns is not None:
self.kineticsConstrainedReactions = sim_data.process.metabolism.kineticTargetShuffleRxns
self.active_constraints_mask = np.ones(len(
self.kineticsConstrainedReactions),
dtype=bool)
else:
constrainedReactionList = sim_data.process.metabolism.constrainedReactionList
constraintsToDisable = sim_data.process.metabolism.constraintsToDisable
self.active_constraints_mask = np.array([
(rxn not in constraintsToDisable)
for rxn in constrainedReactionList
])
self.kineticsConstrainedReactions = list(
np.array(constrainedReactionList)[
self.active_constraints_mask])
self.kineticsEnzymesList = sim_data.process.metabolism.enzymeIdList
self.kineticsSubstratesList = sim_data.process.metabolism.kineticsSubstratesList
constraintToReactionMatrixI = sim_data.process.metabolism.constraintToReactionMatrixI
constraintToReactionMatrixJ = sim_data.process.metabolism.constraintToReactionMatrixJ
constraintToReactionMatrixV = sim_data.process.metabolism.constraintToReactionMatrixV
shape = (constraintToReactionMatrixI.max() + 1,
constraintToReactionMatrixJ.max() + 1)
self.constraintToReactionMatrix = np.zeros(shape, np.float64)
self.constraintToReactionMatrix[
constraintToReactionMatrixI,
constraintToReactionMatrixJ] = constraintToReactionMatrixV
self.constraintIsKcatOnly = sim_data.process.metabolism.constraintIsKcatOnly
# Set solver and kinetic objective weight (lambda)
solver = sim_data.process.metabolism.solver
kinetic_objective_weight = sim_data.process.metabolism.kinetic_objective_weight
# Disable kinetics completely if weight is 0 or specified in file above
self.use_kinetics = True
if not USE_KINETICS or kinetic_objective_weight == 0:
self.use_kinetics = False
kinetic_objective_weight = 0
# Set up FBA solver
# reactionRateTargets value is just for initialization, it gets reset each timestep during evolveState
self.fbaObjectOptions = {
"reactionStoich":
sim_data.process.metabolism.reactionStoich,
"externalExchangedMolecules":
externalExchangedMolecules,
"objective":
self.homeostaticObjective,
"objectiveType":
"homeostatic_kinetics_mixed",
"objectiveParameters": {
"kineticObjectiveWeight": kinetic_objective_weight,
"reactionRateTargets": {
reaction: 1
for reaction in self.kineticsConstrainedReactions
},
"oneSidedReactionTargets": [],
},
"moleculeMasses":
moleculeMasses,
"secretionPenaltyCoeff":
sim_data.constants.
secretion_penalty_coeff, # The "inconvenient constant"--limit secretion (e.g., of CO2)
"solver":
solver,
"maintenanceCostGAM":
energyCostPerWetMass.asNumber(COUNTS_UNITS / MASS_UNITS),
"maintenanceReaction":
sim_data.process.metabolism.maintenanceReaction,
}
if not self.use_kinetics:
self.fbaObjectOptions["objectiveType"] = "homeostatic"
self.fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
self.internalExchangeIdxs = np.array([
self.metaboliteNamesFromNutrients.index(x)
for x in self.fba.getOutputMoleculeIDs()
])
# Disable all rates during burn-in
if self.use_kinetics:
if KINETICS_BURN_IN_PERIOD > 0:
self.fba.disableKineticTargets()
self.burnInComplete = False
else:
self.burnInComplete = True
# Values will get updated at each time point
self.currentNgam = 1 * (COUNTS_UNITS / VOLUME_UNITS)
self.currentPolypeptideElongationEnergy = 1 * (COUNTS_UNITS /
VOLUME_UNITS)
# External molecules
self.externalMoleculeIDs = self.fba.getExternalMoleculeIDs()
# Views
self.metaboliteNames = self.fba.getOutputMoleculeIDs()
self.metabolites = self.bulkMoleculesView(
self.metaboliteNamesFromNutrients)
self.catalysts = self.bulkMoleculesView(self.catalystsList)
self.kineticsEnzymes = self.bulkMoleculesView(self.kineticsEnzymesList)
self.kineticsSubstrates = self.bulkMoleculesView(
self.kineticsSubstratesList)
outputMoleculeIDs = self.fba.getOutputMoleculeIDs()
assert outputMoleculeIDs == self.fba.getInternalMoleculeIDs()
# Set the priority to a low value
self.bulkMoleculesRequestPriorityIs(REQUEST_PRIORITY_METABOLISM)
self.AAs = [
x[:-3] for x in sorted(sim_data.amino_acid_1_to_3_ordered.values())
]
self.shuffleIdxs = None
if hasattr(
sim_data.process.metabolism, "kineticTargetShuffleIdxs"
) and sim_data.process.metabolism.kineticTargetShuffleIdxs is not None:
self.shuffleIdxs = sim_data.process.metabolism.kineticTargetShuffleIdxs
self.shuffleCatalyzedIdxs = None
if hasattr(
sim_data.process.metabolism, "catalystShuffleIdxs"
) and sim_data.process.metabolism.catalystShuffleIdxs is not None:
self.shuffleCatalyzedIdxs = sim_data.process.metabolism.catalystShuffleIdxs
self.run_flux_sensitivity = getattr(sim_data.process.metabolism,
'run_flux_sensitivity', False)
def calculateRequest(self):
self.metabolites.requestAll()
self.catalysts.requestAll()
self.kineticsEnzymes.requestAll()
self.kineticsSubstrates.requestAll()
def evolveState(self):
metaboliteCountsInit = self.metabolites.counts()
cellMass = (self.readFromListener("Mass", "cellMass") * units.fg)
dryMass = (self.readFromListener("Mass", "dryMass") * units.fg)
cellVolume = cellMass / self.cellDensity
countsToMolar = 1 / (self.nAvogadro * cellVolume)
current_nutrients = self._external_states['Environment'].nutrients
self.concModificationsBasedOnCondition = self.getBiomassAsConcentrations(
self.nutrientToDoublingTime.get(
current_nutrients, self.nutrientToDoublingTime["minimal"]))
# Coefficient to convert between flux (mol/g DCW/hr) basis and concentration (M) basis
coefficient = dryMass / cellMass * self.cellDensity * (
self.timeStepSec() * units.s)
# Set external molecule levels
externalMoleculeLevels, newObjective = self.exchangeConstraints(
self.externalMoleculeIDs,
coefficient,
COUNTS_UNITS / VOLUME_UNITS,
current_nutrients,
self.concModificationsBasedOnCondition,
)
updatedObjective = False
if newObjective != None and newObjective != self.homeostaticObjective:
# Build new fba instance with new objective
self.fbaObjectOptions["objective"] = newObjective
self.fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
self.internalExchangeIdxs = np.array([
self.metaboliteNamesFromNutrients.index(x)
for x in self.fba.getOutputMoleculeIDs()
])
self.homeostaticObjective = newObjective
updatedObjective = True
# After completing the burn-in, enable kinetic rates
if self.use_kinetics and (not self.burnInComplete) and (
self._sim.time() > KINETICS_BURN_IN_PERIOD):
self.burnInComplete = True
self.fba.enableKineticTargets()
# Allow flexibility for solver in first time step after an environment shift
if updatedObjective:
self.fba.disableKineticTargets()
self.burnInComplete = False
# Find metabolite concentrations from metabolite counts
metaboliteConcentrations = countsToMolar * metaboliteCountsInit[
self.internalExchangeIdxs]
# Make a dictionary of metabolite names to metabolite concentrations
metaboliteConcentrationsDict = dict(
zip(self.metaboliteNames, metaboliteConcentrations))
self.fba.setInternalMoleculeLevels(
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
# Set external molecule levels
self._setExternalMoleculeLevels(self.fba, externalMoleculeLevels,
metaboliteConcentrations)
# Change the ngam and polypeptide elongation energy penalty only if they are noticably different from the current value
ADJUSTMENT_RATIO = .01
# Calculate new NGAM and update if necessary
self.newNgam = self.ngam * coefficient
ngam_diff = np.abs(self.currentNgam.asNumber() -
self.newNgam.asNumber()) / (
self.currentNgam.asNumber() + 1e-20)
if ngam_diff > ADJUSTMENT_RATIO:
self.currentNgam = self.newNgam
flux = (self.ngam * coefficient).asNumber(COUNTS_UNITS /
VOLUME_UNITS)
self.fba.setReactionFluxBounds(self.fba._reactionID_NGAM,
lowerBounds=flux,
upperBounds=flux)
# Calculate GTP usage based on how much was needed in polypeptide elongation in previous step and update if necessary
newPolypeptideElongationEnergy = countsToMolar * 0
if hasattr(self._sim.processes["PolypeptideElongation"], "gtpRequest"):
newPolypeptideElongationEnergy = countsToMolar * self._sim.processes[
"PolypeptideElongation"].gtpRequest
poly_diff = np.abs(
(self.currentPolypeptideElongationEnergy.asNumber() -
newPolypeptideElongationEnergy.asNumber())) / (
self.currentPolypeptideElongationEnergy.asNumber() + 1e-20)
if poly_diff > ADJUSTMENT_RATIO:
self.currentPolypeptideElongationEnergy = newPolypeptideElongationEnergy
flux = self.currentPolypeptideElongationEnergy.asNumber(
COUNTS_UNITS / VOLUME_UNITS)
self.fba.setReactionFluxBounds(
self.fba._reactionID_polypeptideElongationEnergy,
lowerBounds=flux,
upperBounds=flux)
# Constrain reactions based on absence of catalysts
## Read counts for catalysts and enzymes (catalysts with kinetics constraints)
catalystsCountsInit = self.catalysts.counts()
## Set hard upper bounds constraints based on enzyme presence (infinite upper bound) or absence (upper bound of zero)
catalyzedReactionBounds = np.inf * np.ones(
len(self.reactionsWithCatalystsList))
rxnPresence = self.catalysisMatrix.dot(catalystsCountsInit)
catalyzedReactionBounds[rxnPresence == 0] = 0
if self.shuffleCatalyzedIdxs is not None:
catalyzedReactionBounds = catalyzedReactionBounds[
self.shuffleCatalyzedIdxs]
## Only update reaction limits that are different from previous time step
updateIdxs = np.where(
catalyzedReactionBounds != self.catalyzedReactionBoundsPrev)[0]
updateRxns = [
self.reactionsWithCatalystsList[idx] for idx in updateIdxs
]
updateVals = catalyzedReactionBounds[updateIdxs]
self.fba.setReactionFluxBounds(updateRxns,
upperBounds=updateVals,
raiseForReversible=False)
self.catalyzedReactionBoundsPrev = catalyzedReactionBounds
# Constrain reactions based on kinetic values
kineticsEnzymesCountsInit = self.kineticsEnzymes.counts()
kineticsEnzymesConcentrations = countsToMolar * kineticsEnzymesCountsInit
kineticsSubstratesCountsInit = self.kineticsSubstrates.counts()
kineticsSubstratesConcentrations = countsToMolar * kineticsSubstratesCountsInit
## Set target fluxes for reactions based on their most relaxed constraint
constraintValues = self.getKineticConstraints(
kineticsEnzymesConcentrations.asNumber(units.umol / units.L),
kineticsSubstratesConcentrations.asNumber(units.umol / units.L),
)
reactionTargets = (units.umol / units.L / units.s) * np.max(
self.constraintToReactionMatrix * constraintValues, axis=1)
## Shuffle parameters (only performed in very specific cases)
if self.shuffleIdxs is not None:
reactionTargets = (units.umol / units.L / units.s
) * reactionTargets.asNumber()[self.shuffleIdxs]
## Record which constraint was used, add constraintToReactionMatrix to ensure the index is one of the constraints if multiplication is 0
reactionConstraint = np.argmax(
self.constraintToReactionMatrix * constraintValues +
self.constraintToReactionMatrix,
axis=1)
## Calculate reaction flux target for current time step
targets = (TIME_UNITS * self.timeStepSec() * reactionTargets).asNumber(
COUNTS_UNITS / VOLUME_UNITS)[self.active_constraints_mask]
## Set kinetic targets only if kinetics is enabled
if self.use_kinetics and self.burnInComplete:
self.fba.setKineticTarget(self.kineticsConstrainedReactions,
targets,
raiseForReversible=False)
# Runs sensitivity if option is set
# Needs to be after all FBA problem setup but will not affect simulation
if self.run_flux_sensitivity:
self.flux_sensitivity(metaboliteConcentrations,
externalMoleculeLevels,
catalyzedReactionBounds, targets,
coefficient)
# Solve FBA problem and update metabolite counts
deltaMetabolites = (
1 / countsToMolar) * (COUNTS_UNITS / VOLUME_UNITS *
self.fba.getOutputMoleculeLevelsChange())
metaboliteCountsFinal = np.zeros_like(metaboliteCountsInit)
metaboliteCountsFinal[self.internalExchangeIdxs] = np.fmax(
stochasticRound(
self.randomState,
metaboliteCountsInit[self.internalExchangeIdxs] +
deltaMetabolites.asNumber()), 0).astype(np.int64)
self.metabolites.countsIs(metaboliteCountsFinal)
exFluxes = ((COUNTS_UNITS / VOLUME_UNITS) *
self.fba.getExternalExchangeFluxes() /
coefficient).asNumber(units.mmol / units.g / units.h)
# Write outputs to listeners
self.writeToListener("FBAResults", "deltaMetabolites",
metaboliteCountsFinal - metaboliteCountsInit)
self.writeToListener("FBAResults", "reactionFluxes",
self.fba.getReactionFluxes() / self.timeStepSec())
self.writeToListener("FBAResults", "externalExchangeFluxes", exFluxes)
self.writeToListener("FBAResults", "objectiveValue",
self.fba.getObjectiveValue())
self.writeToListener("FBAResults", "shadowPrices",
self.fba.getShadowPrices(self.metaboliteNames))
self.writeToListener(
"FBAResults", "reducedCosts",
self.fba.getReducedCosts(self.fba.getReactionIDs()))
self.writeToListener("FBAResults", "targetConcentrations", [
self.homeostaticObjective[mol]
for mol in self.fba.getHomeostaticTargetMolecules()
])
self.writeToListener("FBAResults", "homeostaticObjectiveValues",
self.fba.getHomeostaticObjectiveValues())
self.writeToListener("FBAResults", "kineticObjectiveValues",
self.fba.getKineticObjectiveValues())
self.writeToListener("EnzymeKinetics", "metaboliteCountsInit",
metaboliteCountsInit)
self.writeToListener("EnzymeKinetics", "metaboliteCountsFinal",
metaboliteCountsFinal)
self.writeToListener("EnzymeKinetics", "enzymeCountsInit",
kineticsEnzymesCountsInit)
self.writeToListener(
"EnzymeKinetics", "metaboliteConcentrations",
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.writeToListener(
"EnzymeKinetics", "countsToMolar",
countsToMolar.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.writeToListener(
"EnzymeKinetics", "actualFluxes",
self.fba.getReactionFluxes(self.kineticsConstrainedReactions) /
self.timeStepSec())
self.writeToListener("EnzymeKinetics", "targetFluxes",
targets / self.timeStepSec())
self.writeToListener("EnzymeKinetics", "reactionConstraint",
reactionConstraint[self.active_constraints_mask])
def flux_sensitivity(self, metaboliteConcentrations,
externalMoleculeLevels, catalyzedReactionBounds,
targets, coefficient):
"""
Sensitivity performed with flux_sensitivity variant simulations. Disables
kinetic constraints one by one to determine impact on certain fluxes.
Args:
metaboliteConcentrations (ndarray[float] with mol/volume units):
concentrations for all metabolites
externalMoleculeLevels (ndarray[float]): limits for external
molecule exchanges
catalyzedReactionBounds (ndarray[float]): max limit for each
enzyme catalyzed reaction
targets (ndarray[float]): flux target for each reaction with a
kinetic constraint
coefficient (float with mass-time/volume units): conversion factor
for fluxes
"""
succ_rxn = 'SUCCINATE-DEHYDROGENASE-UBIQUINONE-RXN-SUC/UBIQUINONE-8//FUM/CPD-9956.31.'
iso_rxn = 'ISOCITDEH-RXN'
succ_flux = []
iso_flux = []
for rxn in self.kineticsConstrainedReactions:
fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
fba.enableKineticTargets()
fba.setInternalMoleculeLevels(
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self._setExternalMoleculeLevels(fba, externalMoleculeLevels,
metaboliteConcentrations)
flux = (self.ngam * coefficient).asNumber(COUNTS_UNITS /
VOLUME_UNITS)
fba.setReactionFluxBounds(fba._reactionID_NGAM,
lowerBounds=flux,
upperBounds=flux)
flux = self.currentPolypeptideElongationEnergy.asNumber(
COUNTS_UNITS / VOLUME_UNITS)
fba.setReactionFluxBounds(
self.fba._reactionID_polypeptideElongationEnergy,
lowerBounds=flux,
upperBounds=flux)
fba.setReactionFluxBounds(self.reactionsWithCatalystsList,
upperBounds=catalyzedReactionBounds,
raiseForReversible=False)
fba.setKineticTarget(self.kineticsConstrainedReactions,
targets,
raiseForReversible=False)
fba.disableKineticTargets(rxn)
# Fluxes of interest for each disabled constraint
succ_flux += [
((COUNTS_UNITS / VOLUME_UNITS) *
fba.getReactionFluxes(succ_rxn)[0] / coefficient).asNumber(
units.mmol / units.g / units.h)
]
iso_flux += [
((COUNTS_UNITS / VOLUME_UNITS) *
fba.getReactionFluxes(iso_rxn)[0] / coefficient).asNumber(
units.mmol / units.g / units.h)
]
# Fluxes of interest for the original simulation
succ_flux += [((COUNTS_UNITS / VOLUME_UNITS) *
self.fba.getReactionFluxes(succ_rxn)[0] /
coefficient).asNumber(units.mmol / units.g / units.h)]
iso_flux += [((COUNTS_UNITS / VOLUME_UNITS) *
self.fba.getReactionFluxes(iso_rxn)[0] /
coefficient).asNumber(units.mmol / units.g / units.h)]
self.writeToListener('FBAResults', 'succinate_flux_sensitivity',
np.array(succ_flux))
self.writeToListener('FBAResults', 'isocitrate_flux_sensitivity',
np.array(iso_flux))
# limit amino acid uptake to what is needed to meet concentration objective to prevent use as carbon source
def _setExternalMoleculeLevels(self, fba, externalMoleculeLevels,
metaboliteConcentrations):
for aa in self.AAs:
if aa + "[p]" in fba.getExternalMoleculeIDs():
idx = self.externalMoleculeIDs.index(aa + "[p]")
elif aa + "[c]" in fba.getExternalMoleculeIDs():
idx = self.externalMoleculeIDs.index(aa + "[c]")
else:
continue
concDiff = self.homeostaticObjective[
aa + "[c]"] - metaboliteConcentrations[
self.metaboliteNames.index(aa + "[c]")].asNumber(
COUNTS_UNITS / VOLUME_UNITS)
if concDiff < 0:
concDiff = 0
if externalMoleculeLevels[idx] > concDiff:
externalMoleculeLevels[idx] = concDiff
fba.setExternalMoleculeLevels(externalMoleculeLevels)
def evolveState(self):
metaboliteCountsInit = self.metabolites.counts()
cellMass = (self.readFromListener("Mass", "cellMass") * units.fg)
dryMass = (self.readFromListener("Mass", "dryMass") * units.fg)
cellVolume = cellMass / self.cellDensity
countsToMolar = 1 / (self.nAvogadro * cellVolume)
current_nutrients = self._external_states['Environment'].nutrients
self.concModificationsBasedOnCondition = self.getBiomassAsConcentrations(
self.nutrientToDoublingTime.get(
current_nutrients, self.nutrientToDoublingTime["minimal"]))
# Coefficient to convert between flux (mol/g DCW/hr) basis and concentration (M) basis
coefficient = dryMass / cellMass * self.cellDensity * (
self.timeStepSec() * units.s)
# Set external molecule levels
externalMoleculeLevels, newObjective = self.exchangeConstraints(
self.externalMoleculeIDs,
coefficient,
COUNTS_UNITS / VOLUME_UNITS,
current_nutrients,
self.concModificationsBasedOnCondition,
)
updatedObjective = False
if newObjective != None and newObjective != self.homeostaticObjective:
# Build new fba instance with new objective
self.fbaObjectOptions["objective"] = newObjective
self.fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
self.internalExchangeIdxs = np.array([
self.metaboliteNamesFromNutrients.index(x)
for x in self.fba.getOutputMoleculeIDs()
])
self.homeostaticObjective = newObjective
updatedObjective = True
# After completing the burn-in, enable kinetic rates
if self.use_kinetics and (not self.burnInComplete) and (
self._sim.time() > KINETICS_BURN_IN_PERIOD):
self.burnInComplete = True
self.fba.enableKineticTargets()
# Allow flexibility for solver in first time step after an environment shift
if updatedObjective:
self.fba.disableKineticTargets()
self.burnInComplete = False
# Find metabolite concentrations from metabolite counts
metaboliteConcentrations = countsToMolar * metaboliteCountsInit[
self.internalExchangeIdxs]
# Make a dictionary of metabolite names to metabolite concentrations
metaboliteConcentrationsDict = dict(
zip(self.metaboliteNames, metaboliteConcentrations))
self.fba.setInternalMoleculeLevels(
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
# Set external molecule levels
self._setExternalMoleculeLevels(self.fba, externalMoleculeLevels,
metaboliteConcentrations)
# Change the ngam and polypeptide elongation energy penalty only if they are noticably different from the current value
ADJUSTMENT_RATIO = .01
# Calculate new NGAM and update if necessary
self.newNgam = self.ngam * coefficient
ngam_diff = np.abs(self.currentNgam.asNumber() -
self.newNgam.asNumber()) / (
self.currentNgam.asNumber() + 1e-20)
if ngam_diff > ADJUSTMENT_RATIO:
self.currentNgam = self.newNgam
flux = (self.ngam * coefficient).asNumber(COUNTS_UNITS /
VOLUME_UNITS)
self.fba.setReactionFluxBounds(self.fba._reactionID_NGAM,
lowerBounds=flux,
upperBounds=flux)
# Calculate GTP usage based on how much was needed in polypeptide elongation in previous step and update if necessary
newPolypeptideElongationEnergy = countsToMolar * 0
if hasattr(self._sim.processes["PolypeptideElongation"], "gtpRequest"):
newPolypeptideElongationEnergy = countsToMolar * self._sim.processes[
"PolypeptideElongation"].gtpRequest
poly_diff = np.abs(
(self.currentPolypeptideElongationEnergy.asNumber() -
newPolypeptideElongationEnergy.asNumber())) / (
self.currentPolypeptideElongationEnergy.asNumber() + 1e-20)
if poly_diff > ADJUSTMENT_RATIO:
self.currentPolypeptideElongationEnergy = newPolypeptideElongationEnergy
flux = self.currentPolypeptideElongationEnergy.asNumber(
COUNTS_UNITS / VOLUME_UNITS)
self.fba.setReactionFluxBounds(
self.fba._reactionID_polypeptideElongationEnergy,
lowerBounds=flux,
upperBounds=flux)
# Constrain reactions based on absence of catalysts
## Read counts for catalysts and enzymes (catalysts with kinetics constraints)
catalystsCountsInit = self.catalysts.counts()
## Set hard upper bounds constraints based on enzyme presence (infinite upper bound) or absence (upper bound of zero)
catalyzedReactionBounds = np.inf * np.ones(
len(self.reactionsWithCatalystsList))
rxnPresence = self.catalysisMatrix.dot(catalystsCountsInit)
catalyzedReactionBounds[rxnPresence == 0] = 0
if self.shuffleCatalyzedIdxs is not None:
catalyzedReactionBounds = catalyzedReactionBounds[
self.shuffleCatalyzedIdxs]
## Only update reaction limits that are different from previous time step
updateIdxs = np.where(
catalyzedReactionBounds != self.catalyzedReactionBoundsPrev)[0]
updateRxns = [
self.reactionsWithCatalystsList[idx] for idx in updateIdxs
]
updateVals = catalyzedReactionBounds[updateIdxs]
self.fba.setReactionFluxBounds(updateRxns,
upperBounds=updateVals,
raiseForReversible=False)
self.catalyzedReactionBoundsPrev = catalyzedReactionBounds
# Constrain reactions based on kinetic values
kineticsEnzymesCountsInit = self.kineticsEnzymes.counts()
kineticsEnzymesConcentrations = countsToMolar * kineticsEnzymesCountsInit
kineticsSubstratesCountsInit = self.kineticsSubstrates.counts()
kineticsSubstratesConcentrations = countsToMolar * kineticsSubstratesCountsInit
## Set target fluxes for reactions based on their most relaxed constraint
constraintValues = self.getKineticConstraints(
kineticsEnzymesConcentrations.asNumber(units.umol / units.L),
kineticsSubstratesConcentrations.asNumber(units.umol / units.L),
)
reactionTargets = (units.umol / units.L / units.s) * np.max(
self.constraintToReactionMatrix * constraintValues, axis=1)
## Shuffle parameters (only performed in very specific cases)
if self.shuffleIdxs is not None:
reactionTargets = (units.umol / units.L / units.s
) * reactionTargets.asNumber()[self.shuffleIdxs]
## Record which constraint was used, add constraintToReactionMatrix to ensure the index is one of the constraints if multiplication is 0
reactionConstraint = np.argmax(
self.constraintToReactionMatrix * constraintValues +
self.constraintToReactionMatrix,
axis=1)
## Calculate reaction flux target for current time step
targets = (TIME_UNITS * self.timeStepSec() * reactionTargets).asNumber(
COUNTS_UNITS / VOLUME_UNITS)[self.active_constraints_mask]
## Set kinetic targets only if kinetics is enabled
if self.use_kinetics and self.burnInComplete:
self.fba.setKineticTarget(self.kineticsConstrainedReactions,
targets,
raiseForReversible=False)
# Runs sensitivity if option is set
# Needs to be after all FBA problem setup but will not affect simulation
if self.run_flux_sensitivity:
self.flux_sensitivity(metaboliteConcentrations,
externalMoleculeLevels,
catalyzedReactionBounds, targets,
coefficient)
# Solve FBA problem and update metabolite counts
deltaMetabolites = (
1 / countsToMolar) * (COUNTS_UNITS / VOLUME_UNITS *
self.fba.getOutputMoleculeLevelsChange())
metaboliteCountsFinal = np.zeros_like(metaboliteCountsInit)
metaboliteCountsFinal[self.internalExchangeIdxs] = np.fmax(
stochasticRound(
self.randomState,
metaboliteCountsInit[self.internalExchangeIdxs] +
deltaMetabolites.asNumber()), 0).astype(np.int64)
self.metabolites.countsIs(metaboliteCountsFinal)
exFluxes = ((COUNTS_UNITS / VOLUME_UNITS) *
self.fba.getExternalExchangeFluxes() /
coefficient).asNumber(units.mmol / units.g / units.h)
# Write outputs to listeners
self.writeToListener("FBAResults", "deltaMetabolites",
metaboliteCountsFinal - metaboliteCountsInit)
self.writeToListener("FBAResults", "reactionFluxes",
self.fba.getReactionFluxes() / self.timeStepSec())
self.writeToListener("FBAResults", "externalExchangeFluxes", exFluxes)
self.writeToListener("FBAResults", "objectiveValue",
self.fba.getObjectiveValue())
self.writeToListener("FBAResults", "shadowPrices",
self.fba.getShadowPrices(self.metaboliteNames))
self.writeToListener(
"FBAResults", "reducedCosts",
self.fba.getReducedCosts(self.fba.getReactionIDs()))
self.writeToListener("FBAResults", "targetConcentrations", [
self.homeostaticObjective[mol]
for mol in self.fba.getHomeostaticTargetMolecules()
])
self.writeToListener("FBAResults", "homeostaticObjectiveValues",
self.fba.getHomeostaticObjectiveValues())
self.writeToListener("FBAResults", "kineticObjectiveValues",
self.fba.getKineticObjectiveValues())
self.writeToListener("EnzymeKinetics", "metaboliteCountsInit",
metaboliteCountsInit)
self.writeToListener("EnzymeKinetics", "metaboliteCountsFinal",
metaboliteCountsFinal)
self.writeToListener("EnzymeKinetics", "enzymeCountsInit",
kineticsEnzymesCountsInit)
self.writeToListener(
"EnzymeKinetics", "metaboliteConcentrations",
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.writeToListener(
"EnzymeKinetics", "countsToMolar",
countsToMolar.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.writeToListener(
"EnzymeKinetics", "actualFluxes",
self.fba.getReactionFluxes(self.kineticsConstrainedReactions) /
self.timeStepSec())
self.writeToListener("EnzymeKinetics", "targetFluxes",
targets / self.timeStepSec())
self.writeToListener("EnzymeKinetics", "reactionConstraint",
reactionConstraint[self.active_constraints_mask])
def test_standard_IDs(self):
fba = FluxBalanceAnalysis(**_testStandard)
self.assertEqual(set(fba.getExternalMoleculeIDs()), {"A", "D"})
self.assertEqual(set(fba.getOutputMoleculeIDs()), {"B", "C", "E"})
def test_instantiation(self):
fba = FluxBalanceAnalysis(**_testStandard)
"E": 20,
"F": 20,
"G": 20,
"H": 20,
"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