-
Notifications
You must be signed in to change notification settings - Fork 0
/
metabolism.py
412 lines (312 loc) · 17.5 KB
/
metabolism.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
#!/usr/bin/env python
"""
Metabolism
Metabolism sub-model. Encodes molecular simulation of microbial metabolism using flux-balance analysis.
TODO:
- option to call a reduced form of metabolism (assume optimal)
@author: Derek Macklin
@organization: Covert Lab, Department of Bioengineering, Stanford University
@date: Created 4/2/2013
"""
from __future__ import division
from itertools import izip
import numpy as np
from scipy.sparse import csr_matrix
import wholecell.processes.process
from wholecell.utils import units
from wholecell.utils.random import stochasticRound
from wholecell.utils.constants import REQUEST_PRIORITY_METABOLISM
from wholecell.utils.modular_fba import FluxBalanceAnalysis
from wholecell.utils.enzymeKinetics import EnzymeKinetics
from wholecell.utils.fitting import massesAndCountsToAddForHomeostaticTargets
COUNTS_UNITS = units.dmol
VOLUME_UNITS = units.L
MASS_UNITS = units.g
TIME_UNITS = units.s
FLUX_UNITS = COUNTS_UNITS / VOLUME_UNITS / TIME_UNITS
SECRETION_PENALTY_COEFF = 1e-5
NONZERO_ENZYMES = False
USE_KINETIC_RATES = True
USE_BASE_RATES = True
KINETICS_BURN_IN_PERIOD = 1
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.exchangeConstraints = sim_data.process.metabolism.exchangeConstraints
self.doublingTime = sim_data.doubling_time
self.nutrientsTimeSeriesLabel = sim_data.nutrientsTimeSeriesLabel
# Load enzyme kinetic rate information
self.reactionRateInfo = sim_data.process.metabolism.reactionRateInfo
self.enzymeNames = sim_data.process.metabolism.metabolicEnzymes
self.constraintIDs = sim_data.process.metabolism.constraintIDs
self.constraintMultiplesDict = {constraintID:rateInfo["constraintMultiple"] for constraintID, rateInfo in self.reactionRateInfo.iteritems()}
self.constraintToReactionDict = sim_data.process.metabolism.constraintToReactionDict
self.reactionEnzymes = sim_data.process.metabolism.reactionEnzymes
concDict = sim_data.process.metabolism.concentrationUpdates.concentrationsBasedOnNutrients(
sim_data.nutrientsTimeSeries[sim_data.nutrientsTimeSeriesLabel][0][1]
)
self.concModificationsBasedOnCondition = sim_data.mass.getBiomassAsConcentrations(sim_data.conditionToDoublingTime[sim_data.condition])
concDict.update(self.concModificationsBasedOnCondition)
self.objective = dict(
(key, concDict[key].asNumber(COUNTS_UNITS / VOLUME_UNITS)) for key in concDict
)
self.getMass = sim_data.getter.getMass
self.massReconstruction = sim_data.mass
self.avgCellToInitialCellConvFactor = sim_data.mass.avgCellToInitialCellConvFactor
self.ngam = sim_data.constants.nonGrowthAssociatedMaintenance
initWaterMass = sim_data.mass.avgCellWaterMassInit
initDryMass = sim_data.mass.avgCellDryMassInit
initCellMass = (
initWaterMass
+ initDryMass
)
self.energyCostPerWetMass = sim_data.constants.darkATP * initDryMass / initCellMass
self.reactionStoich = sim_data.process.metabolism.reactionStoich
self.externalExchangeMolecules = sim_data.nutrientData["secretionExchangeMolecules"]
for time, nutrientsLabel in sim_data.nutrientsTimeSeries[self.nutrientsTimeSeriesLabel]:
self.externalExchangeMolecules += sim_data.nutrientData["importExchangeMolecules"][nutrientsLabel]
self.maintenanceReaction = sim_data.process.metabolism.maintenanceReaction
self.externalExchangeMolecules = sorted(self.externalExchangeMolecules)
self.extMoleculeMasses = self.getMass(self.externalExchangeMolecules)
self.moleculeMasses = dict(zip(
self.externalExchangeMolecules,
self.getMass(self.externalExchangeMolecules).asNumber(MASS_UNITS / COUNTS_UNITS)
))
# Set up enzyme kinetics object
self.enzymeKinetics = EnzymeKinetics(
reactionRateInfo = sim_data.process.metabolism.reactionRateInfo,
useCustoms=True,
moreThanKcat=False, # Only compute rates for reactions with more than a kcat
)
# Remove kinetics for reactions for which we don't have needed metabolites or enzymes
metaboliteSMatrixNames = set()
for stoich in self.reactionStoich.values():
metaboliteSMatrixNames.update(stoich.keys())
metaboliteSMatrixNames = sorted(metaboliteSMatrixNames)
self.enzymeKinetics.checkKnownSubstratesAndEnzymes(metaboliteSMatrixNames, sim_data.process.metabolism.concDict, self.enzymeNames, removeUnknowns=True)
# Add reactions with a kinetic estimate
self.allRateReactions = sorted(set([reactionInfo["reactionID"] for constraintID, reactionInfo in self.enzymeKinetics.reactionRateInfo.iteritems() if reactionInfo["reactionID"] in self.reactionStoich]))
# Reactions with full kinetic estimates (more than just kcat)
self.fullRateReactions = sorted(set([reactionInfo["reactionID"] for constraintID, reactionInfo in self.enzymeKinetics.reactionRateInfo.iteritems() if (len(reactionInfo["kM"]) > 0 or reactionInfo["rateEquationType"] == "custom") and reactionInfo["reactionID"] in self.reactionStoich]))
# Reactions with a kcat-based kinetic estimate only (no customs, no kMs, no kIs)
self.kcatRateReactions = sorted(set([reactionInfo["reactionID"] for constraintID, reactionInfo in self.enzymeKinetics.reactionRateInfo.iteritems() if reactionInfo["reactionID"] not in self.fullRateReactions and reactionInfo["reactionID"] in self.reactionStoich]))
print "len(self.allRateReactions)"
print len(self.allRateReactions)
self.metabolismKineticObjectiveWeight = sim_data.constants.metabolismKineticObjectiveWeight
# Set up FBA solver
self.fbaObjectOptions = {
"reactionStoich" : self.reactionStoich,
"externalExchangedMolecules" : self.externalExchangeMolecules,
"objective" : self.objective,
"objectiveType" : "homeostatic_kinetics_mixed",
"objectiveParameters" : {
"kineticObjectiveWeight":self.metabolismKineticObjectiveWeight,
"reactionRateTargets":{reaction:1e-5 for reaction in self.allRateReactions}, #This target is arbitrary, it gets reset each timestep during evolveState
"oneSidedReactionTargets":self.kcatRateReactions,
},
"moleculeMasses" : self.moleculeMasses,
"secretionPenaltyCoeff" : SECRETION_PENALTY_COEFF, # The "inconvenient constant"--limit secretion (e.g., of CO2)
"solver" : "glpk",
"maintenanceCostGAM" : self.energyCostPerWetMass.asNumber(COUNTS_UNITS / MASS_UNITS),
"maintenanceReaction" : self.maintenanceReaction,
}
if USE_KINETIC_RATES==False:
self.fbaObjectOptions["objectiveType"] = "homeostatic"
self.fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
# Disable all rates during burn-in
if KINETICS_BURN_IN_PERIOD > 0 and USE_KINETIC_RATES:
self.fba.disableKineticTargets()
self.burnInComplete = False
else:
self.burnInComplete = True
# Indices for reactions with full kinetic esimates
self.allRateIndices = np.where([True if reactionID in self.allRateReactions else False for reactionID in self.fba.getReactionIDs()])
self.allRateEstimates = FLUX_UNITS * np.zeros_like(self.allRateIndices)
# Matrix mapping enzymes to the reactions they catalyze
self.enzymeReactionMatrix = sim_data.process.metabolism.enzymeReactionMatrix(
self.fba.getReactionIDs(),
self.enzymeNames,
self.reactionEnzymes,
)
self.spontaneousIndices = np.where(np.sum(self.enzymeReactionMatrix, axis=1) == 0)
self.enzymeReactionMatrix = csr_matrix(self.enzymeReactionMatrix)
self.baseRates = FLUX_UNITS * np.inf * np.ones(len(self.fba.getReactionIDs()))
self.currentNgam = 1 * (COUNTS_UNITS / VOLUME_UNITS)
self.currentPolypeptideElongationEnergy = 1 * (COUNTS_UNITS / VOLUME_UNITS)
# Set constraints
## External molecules
self.externalMoleculeIDs = self.fba.getExternalMoleculeIDs()
# Views
self.metaboliteNames = self.fba.getOutputMoleculeIDs()
self.metabolites = self.bulkMoleculesView(self.metaboliteNames)
self.enzymes = self.bulkMoleculesView(self.enzymeNames)
outputMoleculeIDs = self.fba.getOutputMoleculeIDs()
assert outputMoleculeIDs == self.fba.getInternalMoleculeIDs()
# Set the priority to a low value
self.bulkMoleculesRequestPriorityIs(REQUEST_PRIORITY_METABOLISM)
self.fitterPredictedFluxesDict = sim_data.process.metabolism.predictedFluxesDict
def calculateRequest(self):
self.metabolites.requestAll()
self.enzymes.requestAll()
def evolveState(self):
# Solve for metabolic fluxes
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)
self.newPolypeptideElongationEnergy = countsToMolar * 0
if hasattr(self._sim.processes["PolypeptideElongation"], "gtpRequest"):
self.newPolypeptideElongationEnergy = countsToMolar * self._sim.processes["PolypeptideElongation"].gtpRequest
# Set external molecule levels
coefficient = dryMass / cellMass * self.cellDensity * (self.timeStepSec() * units.s)
externalMoleculeLevels, newObjective = self.exchangeConstraints(
self.externalMoleculeIDs,
coefficient,
COUNTS_UNITS / VOLUME_UNITS,
self.nutrientsTimeSeriesLabel,
self.time(),
self.concModificationsBasedOnCondition,
)
if newObjective != None and newObjective != self.objective:
# Build new fba instance with new objective
self.fbaObjectOptions["objective"] = newObjective
self.fba = FluxBalanceAnalysis(**self.fbaObjectOptions)
# After completing the burn-in, enable kinetic rates
if self._sim.time() > KINETICS_BURN_IN_PERIOD and USE_KINETIC_RATES and not self.burnInComplete:
self.burnInComplete = True
self.fba.enableKineticTargets()
# Set external molecule levels
self.fba.setExternalMoleculeLevels(externalMoleculeLevels)
self.newNgam = self.ngam * coefficient
# Change the ngam and polypeptide elongation energy penalty only if they are noticably different from the current value
ADJUSTMENT_RATIO = .01
ngam_diff = np.abs(self.currentNgam.asNumber() - self.newNgam.asNumber()) / (self.currentNgam.asNumber() + 1e-20)
if ngam_diff > ADJUSTMENT_RATIO:
self.currentNgam = self.newNgam
self.fba.setMaxReactionFlux(self.fba._reactionID_NGAM, (self.ngam * coefficient).asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.fba.setMinReactionFlux(self.fba._reactionID_NGAM, (self.ngam * coefficient).asNumber(COUNTS_UNITS / VOLUME_UNITS))
poly_diff = np.abs((self.currentPolypeptideElongationEnergy.asNumber() - self.newPolypeptideElongationEnergy.asNumber())) / (self.currentPolypeptideElongationEnergy.asNumber() + 1e-20)
if poly_diff > ADJUSTMENT_RATIO:
self.currentPolypeptideElongationEnergy = self.newPolypeptideElongationEnergy
self.fba.setMaxReactionFlux(self.fba._reactionID_polypeptideElongationEnergy, self.currentPolypeptideElongationEnergy.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.fba.setMinReactionFlux(self.fba._reactionID_polypeptideElongationEnergy, self.currentPolypeptideElongationEnergy.asNumber(COUNTS_UNITS / VOLUME_UNITS))
# Find metabolite concentrations from metabolite counts
metaboliteConcentrations = countsToMolar * metaboliteCountsInit
# Make a dictionary of metabolite names to metabolite concentrations
metaboliteConcentrationsDict = dict(zip(self.metaboliteNames, metaboliteConcentrations))
self.fba.setInternalMoleculeLevels(
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS)
)
# Find enzyme concentrations from enzyme counts
enzymeCountsInit = self.enzymes.counts()
enzymeConcentrations = countsToMolar * enzymeCountsInit
if NONZERO_ENZYMES:
# Add one of every enzyme to ensure none are zero
enzymeConcentrations = countsToMolar * (enzymeCountsInit + 1)
for i in range(len(self.enzymeNames)):
if self.burnInComplete:
enzymeConcentrations = countsToMolar * enzymeCountsInit
enzymeConcentrations[i] = countsToMolar * (enzymeCountsInit[i] + 1)
# Make a dictionary of enzyme names to enzyme concentrations
enzymeConcentrationsDict = dict(zip(self.enzymeNames, enzymeConcentrations))
# When many estimates exist for a reaction, choose the largest
if not hasattr(self, "chosenConstraints") and self.burnInComplete:
# Calculate the constraints in the current conditions
reactionsDict = self.enzymeKinetics.allReactionsDict(metaboliteConcentrationsDict, enzymeConcentrationsDict)
oneSidedReactions = set(self.fba.getKineticOneSidedTargetFluxNames())
self.chosenConstraints = {}
for reactionID, reactionRate in reactionsDict.iteritems():
rateOrderedConstraints = sorted(reactionRate.keys(), key=reactionRate.__getitem__, reverse=False)
kMreactions = [x for x in rateOrderedConstraints if 'kcat' not in x]
if len(kMreactions) > 0:
# Take the highest valued constraint with a kM
constraintID = kMreactions[-1]
elif len(kMreactions) == 0:
# Take the higest valued constraint overall
constraintID = rateOrderedConstraints[-1]
self.chosenConstraints[reactionID] = {
"constraintID":constraintID,
"coefficient":self.constraintMultiplesDict[constraintID],}
if USE_KINETIC_RATES and self.burnInComplete:
self.allRateEstimates = self.enzymeKinetics.ratesView(self.allRateReactions, self.chosenConstraints, metaboliteConcentrationsDict, enzymeConcentrationsDict, raiseIfNotFound=True)
# Make kinetic targets numerical zero instead of actually zero for solver stability
self.allRateEstimates[self.allRateEstimates.asNumber() == 0] = FLUX_UNITS * 1e-20
self.fba.setKineticTarget(self.allRateReactions, (TIME_UNITS*self.timeStepSec()*self.allRateEstimates).asNumber(COUNTS_UNITS/VOLUME_UNITS), raiseForReversible=False)
if USE_BASE_RATES and self.burnInComplete:
# Calculate new rates
self.baseRatesNew = FLUX_UNITS * self.enzymeReactionMatrix.dot(enzymeConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.baseRatesNew[self.spontaneousIndices] = (FLUX_UNITS) * np.inf
# Update allRates
updateLocations = np.where(self.baseRatesNew.asNumber(FLUX_UNITS) != self.baseRates.asNumber(FLUX_UNITS))
updateReactions = self.fba.getReactionIDs()[updateLocations]
updateValues = self.baseRatesNew[updateLocations]
self.baseRates[updateLocations] = updateValues
# Set new reaction rate limits
self.fba.setMaxReactionFluxes(updateReactions, (TIME_UNITS*self.timeStepSec()*updateValues).asNumber(COUNTS_UNITS/VOLUME_UNITS), raiseForReversible = False)
deltaMetabolites = (1 / countsToMolar) * (COUNTS_UNITS / VOLUME_UNITS * self.fba.getOutputMoleculeLevelsChange())
import ipdb; ipdb.set_trace()
if self.burnInComplete:
if deltaMetabolites[self.metaboliteNames.index('THIAMINE-PYROPHOSPHATE[c]')] > 0.1:
print self.enzymeNames[i]
continue
break
metaboliteCountsFinal = np.fmax(stochasticRound(
self.randomState,
metaboliteCountsInit + deltaMetabolites.asNumber()
), 0).astype(np.int64)
self.metabolites.countsIs(metaboliteCountsFinal)
# Use GLPK's dualprimal solver, AFTER the first solution
self.fba._solver._model.set_solver_method_dualprimal()
self.overconstraintMultiples = (self.fba.getReactionFluxes()[self.allRateIndices] / self.allRateEstimates.asNumber(FLUX_UNITS))
exFluxes = ((COUNTS_UNITS / VOLUME_UNITS) * self.fba.getExternalExchangeFluxes() / coefficient).asNumber(units.mmol / units.g / units.h)
# TODO: report as reactions (#) per second & store volume elsewhere
self.writeToListener("FBAResults", "reactionFluxes",
self.fba.getReactionFluxes() / self.timeStepSec())
self.writeToListener("FBAResults", "externalExchangeFluxes",
exFluxes)
# self.writeToListener("FBAResults", "objectiveValue", # TODO
# self.fba.objectiveValue() / deltaMetabolites.size) # divide to normalize by number of metabolites
self.writeToListener("FBAResults", "outputFluxes",
self.fba.getOutputMoleculeLevelsChange() / self.timeStepSec())
self.writeToListener("FBAResults", "rowDualValues",
self.fba.getShadowPrices(self.metaboliteNames))
self.writeToListener("FBAResults", "columnDualValues",
self.fba.getReducedCosts(self.fba.getReactionIDs()))
self.writeToListener("FBAResults", "kineticObjectiveValues",
self.fba.getKineticObjectiveValues())
self.writeToListener("FBAResults", "homeostaticObjectiveValues",
self.fba.getHomeostaticObjectiveValues())
self.writeToListener("FBAResults", "homeostaticObjectiveWeight",
self.fba.getHomeostaticObjectiveWeight())
self.writeToListener("EnzymeKinetics", "baseRates",
self.baseRates.asNumber(FLUX_UNITS))
self.writeToListener("EnzymeKinetics", "reactionKineticPredictions",
self.allRateEstimates.asNumber(FLUX_UNITS))
self.writeToListener("EnzymeKinetics", "overconstraintMultiples",
self.overconstraintMultiples)
self.writeToListener("EnzymeKinetics", "kineticTargetFluxes",
self.fba.kineticTargetFluxes())
self.writeToListener("EnzymeKinetics", "kineticTargetErrors",
self.fba.getKineticTargetFluxErrors())
self.writeToListener("EnzymeKinetics", "kineticTargetRelativeDifferences",
self.fba.getKineticTargetFluxRelativeDifferences())
self.writeToListener("EnzymeKinetics", "metaboliteCountsInit",
metaboliteCountsInit)
self.writeToListener("EnzymeKinetics", "metaboliteCountsFinal",
metaboliteCountsFinal)
self.writeToListener("EnzymeKinetics", "enzymeCountsInit",
enzymeCountsInit)
self.writeToListener("EnzymeKinetics", "metaboliteConcentrations",
metaboliteConcentrations.asNumber(COUNTS_UNITS / VOLUME_UNITS))
self.writeToListener("EnzymeKinetics", "countsToMolar",
countsToMolar.asNumber(COUNTS_UNITS / VOLUME_UNITS))