phi_transition_rate = 0.
phi_transition_KM = 0.
# Concentration dependent transition
psi0 = 0.04
phi0 = 0.04
psi_transition_rate = 0.2
phi_transition_rate = 0.2
psi_transition_KM = 30.0
phi_transition_KM = 5.0
### ECgl model
biomass_ECgl = cme.Biomass(
[biomass0 * pcECgl], {
'growth': [(1, 'biomass_ECgl')],
'dilution': [(ch, None)],
'death': [(death_rate, 'biomass_ECgl')],
'psi_transition': [(-1., 'biomass_ECgl')],
'phi_transition': [(transition_efficiency, 'biomass_ECac')]
})
ex_glucose_ECgl = cme.DMetabolite('glc_D_e', [glucose0], False, {
'glucose_exchange': [(1, 'biomass_ECgl')],
'glucose_fed': [(fb, None)]
})
ex_acetate_ECgl = cme.DMetabolite('ac_e', [acetate0], False,
{'acetate_exchange': [(1, 'biomass_ECgl')]
}) #, 'acetate_fed': [(afb, None)]})
oxygen_exchange_ECgl = cre.DReaction(rxnnames['EX_o2_e'],
cre.FixedBound(vminoxygen, 0), False)
### Only Ac secretion
acetate_exchange_ECgl = cre.DReaction(rxnnames['EX_ac_e'],
def main():
'''
Reproduce results from
** Enjalbert et al 2015
With Single EC model
'''
args = options()
verbose = args.verbose
biomass0 = float(args.biomassi)
pcECgl = float(args.ratioecgl)
thrPc = 0.99999
if pcECgl > thrPc:
pcECgl = thrPc
if pcECgl < (1 - thrPc):
pcECgl = 1 - thrPc
strratioecgl = args.ratioecgl.replace('.', 'p')
maxtime = float(args.tmax)
expcond = 'batch_low_Glc'
glucose0 = 15.0 #mM
acetate0 = 0.0 #mM
fb = 0.
afbG = 0.
afbA = 0.
mpath = args.model
if 'xml' in mpath:
print 'Running dFBA simulation'
else:
print 'Loading simulation file'
model = cPickle.load(open(mpath, 'r'))
exitname = mpath.split('/')[-1].split('.')[0].split('-')
if args.runconsortium:
#return model
titlab = 'Initial biomass %.0f%% ECgl and %.0f%% ECac' % (
pcECgl * 100, (1 - pcECgl) * 100)
if args.runvarmabatch or args.runvarmafedbatch:
plotVarma1994_twoEC(args, model, exitname[2],
'-'.join(exitname[1:]), titlab)
return
plotEnjalbert2015_growth2EC(args, model, exitname[2],
'-'.join(exitname[1:]), titlab)
if args.runecgl:
plotEnjalbert2015_growth1EC(args, model.dmodels['ECgl'],
exitname[2],
'-'.join(exitname[1:]))
if args.runecac:
plotEnjalbert2015_growth1EC(args, model.dmodels['Ecac'],
exitname[2],
'-'.join(exitname[1:]))
if args.runsingle:
if args.runvarmabatch or args.runvarmafedbatch:
plotVarma1994(args, model, exitname[2], '-'.join(exitname[1:]))
if args.runecgl:
plotEnjalbert2015_growth1EC(args, model, exitname[2],
'-'.join(exitname[1:]))
return model
ac_thr = 0.0
t_glc = 0.0
if args.runhighacetate:
print('Simulating Batch Growth on 45mM Acetate as in Enjalbert2015')
expcond = 'batch_high_Ac'
glucose0 = 0.0
acetate0 = 45.0
elif args.runlowacetate:
print('Simulating Batch Growth on 4mM Acetate as in Enjalbert2015')
expcond = 'batch_low_Ac'
glucose0 = 0.0
acetate0 = 4.0
elif args.runmixedacetate:
print(
'Simulating Batch Growth on 15mM Glucose 32mM Acetate as in Enjalbert2015'
)
expcond = 'batch_mixed_Ac'
glucose0 = 15.0
acetate0 = 32.0
elif args.runfedlowacetate:
print(
'Simulating Batch Growth on 15mM Glucose and constant 4mM Acetate as in Enjalbert2015'
)
expcond = 'fedbatch_low_Ac'
glucose0 = 15.0
acetate0 = 0.0
ac_thr = 4.0
t_glc = 4.0
if args.runsingle:
t_glc = 3.8
afbG = 1.0
else:
afbA = 1.0
elif args.runfedhighacetate:
print(
'Simulating Batch Growth on 15mM Glucose and constant 32mM Acetate as in Enjalbert2015'
)
expcond = 'fedbatch_high_Ac'
glucose0 = 15.0
acetate0 = 32.0
ac_thr = 32.0
t_glc = 4.0
if args.runsingle:
t_glc = 3.3
afbG = 1.0
else:
afbA = 1.0
elif args.runvarmabatch:
print('Simulating Batch Growth on Glucose as in Varma1994 Fig.7')
expcond = 'varma_batch'
glucose0 = 10.8
acetate0 = 0.4
elif args.runvarmafedbatch:
print('Simulating Fed Batch Growth on Glucose as in Varma1994 Fig.10')
expcond = 'varma_fedbatch'
glucose0 = 0.82
acetate0 = 0.1
#glucose is continuously provided
#0.2 g/(L*hr)
#1 g = 5.5 mmol
#dGLC/dt = ... + fb ==> units = mmol/hr
fb = 5.5 * 0.2 * volExt #mmol/hr
else:
print('Simulating Batch Growth on 15mM Glucose as in Enjalbert2015')
ename = 'ecoli core'
exitname = '%s-%s-%s' % (ename, expcond, args.label)
bmf = 'BIOMASS_Ecoli_core_w_GAM'
rxnnames = {
'EX_glc_e': 'EX_glc__D_e',
'EX_o2_e': 'EX_o2_e',
'EX_ac_e': 'EX_ac_e'
}
## biomass0 is already gDW
## glucose0 and acetate0 are concentrations (mM)
## dMetabolites are initialized with quantities (mmol)
acetate0 = volExt * acetate0 # mM*L = mmol
glucose0 = volExt * glucose0 # mM*L = mmol
#biomass dilution rate in chemostat, 1/hr
ch = 0.
## Uptake parameters
## Uptake Vmax 10 mmol/g/hr (Gosset, 2005)
## Uptake Km 10 muM = 0.01 mM (Gosset, 2005)
vmaxexglc = 10. #mmol/g/hr
vmaxexace = float(args.vminexace) #10. #mmol/g/hr
kmuptake = 0.01 #mM
vminoxygen = float(args.vminexoxygen)
ubexace = float(args.vmaxexace)
#ubexace = 3.0
# cm.reactions.get_by_id('ATPM').lower_bound --> 8.39
#NGAM = 8.39
death_rate = -1 * float(args.deathrate)
psi0 = 0.
psi_transition_rate = 0.
psi_transition_KM = float(
args.kmtranspsi) #mM; Ac concentration @ transition_rate/2
### ACHTUNG with units!! concentrations in the model!!
if args.psitransition:
psi_transition_rate = float(args.vmaxpsi)
psi0 = float(args.psioffset)
phi0 = 0.
phi_transition_rate = 0.
phi_transition_KM = float(
args.kmtransphi) #mM; Glc concentration @ transition_rate/2
### ACHTUNG with units!! concentrations in the model!!
if args.phitransition:
phi_transition_rate = float(args.vmaxphi)
phi0 = float(args.phioffset)
transition_efficiency = float(args.efftrans)
### ECgl model
biomass_ECgl = cme.Biomass(
[biomass0 * pcECgl], {
'growth': [(1, 'biomass_ECgl')],
'dilution': [(ch, None)],
'death': [(death_rate, 'biomass_ECgl')],
'psi_transition': [(-1., 'biomass_ECgl')],
'phi_transition': [(transition_efficiency, 'biomass_ECac')]
})
ex_glucose_ECgl = cme.DMetabolite('glc_D_e', [glucose0], False, {
'glucose_exchange': [(1, 'biomass_ECgl')],
'glucose_fed': [(fb, None)]
})
ex_acetate_ECgl = cme.DMetabolite(
'ac_e', [acetate0], False, {
'acetate_exchange': [(1, 'biomass_ECgl')],
'acetate_fed': [(afbG, 'biomass_ECgl')]
})
oxygen_exchange_ECgl = cre.DReaction(rxnnames['EX_o2_e'],
cre.FixedBound(vminoxygen, 0), False)
### Only Ac secretion
acetate_exchange_ECgl = cre.DReaction(rxnnames['EX_ac_e'],
cre.FixedBound(0., ubexace), True)
if args.runsingle:
biomass_ECgl = cme.Biomass(
[biomass0 * pcECgl], {
'growth': [(1, 'biomass_ECgl')],
'dilution': [(ch, None)],
'death': [(death_rate, 'biomass_ECgl')]
})
acetate_exchange_ECgl = cre.DReaction(
rxnnames['EX_ac_e'],
cre.MichaelisMenten1(ex_acetate_ECgl,
1,
vmaxexace,
kmuptake,
1,
upperBound=ubexace), False)
glucose_exchange_ECgl = cre.DReaction(
rxnnames['EX_glc_e'],
cre.MichaelisMenten1(ex_glucose_ECgl,
1,
vmaxexglc,
kmuptake,
1,
upperBound=15.), False)
growth_ECgl = cre.DReaction(bmf, cre.FixedBound(0., 1000.))
### ECac model
biomass_ECac = cme.Biomass(
[biomass0 * (1 - pcECgl)], {
'growth': [(1, 'biomass_ECac')],
'dilution': [(ch, None)],
'death': [(death_rate, 'biomass_ECac')],
'psi_transition': [(transition_efficiency, 'biomass_ECgl')],
'phi_transition': [(-1., 'biomass_ECac')]
})
ex_glucose_ECac = cme.DMetabolite(
'glc_D_e', [glucose0], False,
{'glucose_exchange': [(1, 'biomass_ECac')]})
ex_acetate_ECac = cme.DMetabolite(
'ac_e', [acetate0], False, {
'acetate_exchange': [(1, 'biomass_ECac')],
'acetate_fed': [(afbA, 'biomass_ECac')]
})
oxygen_exchange_ECac = cre.DReaction(rxnnames['EX_o2_e'],
cre.FixedBound(vminoxygen, 0), False)
acetate_exchange_ECac = cre.DReaction(
rxnnames['EX_ac_e'],
cre.MichaelisMenten1(ex_acetate_ECac,
1,
vmaxexace,
kmuptake,
1,
upperBound=ubexace), False)
### EX_glc off
glucose_exchange_ECac = cre.DReaction(rxnnames['EX_glc_e'],
cre.FixedBound(0., 0.), False)
growth_ECac = cre.DReaction(bmf, cre.FixedBound(0., 1000.))
#acetate_fed_ECac = cre.DReaction(None, cre.ConcentrationMaintenanceFunction(ex_acetate_ECac, ac_thr, t_glc, linkedReactions=[acetate_exchange_ECac]), True, isODE=True)
acetate_fed_ECac = cre.DReaction(None,
cre.SquareWave(min(9.1, vmaxexace), 20, 1,
t_glc),
True,
isODE=True)
#########################
## Transitions
#########################
#lrxn = (growth_ECgl, 1.)
lrxn = None
#lrxn2 = (growth_ECac, 1.)
lrxn2 = None
hctrans = 5
#hctrans = 1
if args.glthreshold:
glccthr = 13.0
## phi = Glc -> Ac ##
## ECglc senses Ac and transitions
biomass_psi_transition_ECgl = cre.DReaction(
None,
cre.MichaelisMentenLinkedSubstrate(ex_acetate_ECgl,
lowerBound=0.,
maxVelocity=psi_transition_rate,
hillCoeff=hctrans,
mmConstant=psi_transition_KM,
linkedSubstrate=ex_glucose_ECgl,
onThrC=0.,
offThrC=glccthr,
offset=psi0),
True,
isODE=True)
# Need a copy to avoid duplicate entries; TODO: add option for class model to not update object dMetabolite/dReaction
#biomass_psi_transition_ECac = copy.copy(biomass_psi_transition_ECgl)
biomass_psi_transition_ECac = cre.DReaction(
None,
cre.MichaelisMentenLinkedSubstrate(ex_acetate_ECac,
lowerBound=0.,
maxVelocity=psi_transition_rate,
hillCoeff=hctrans,
mmConstant=psi_transition_KM,
linkedSubstrate=ex_glucose_ECac,
onThrC=0.,
offThrC=glccthr,
offset=psi0),
True,
isODE=True)
else:
## phi = Glc -> Ac ##
## ECglc senses Ac and transitions
biomass_psi_transition_ECgl = cre.DReaction(
None,
cre.MichaelisMentenLinked(ex_acetate_ECgl,
lowerBound=0.,
maxVelocity=psi_transition_rate,
hillCoeff=hctrans,
mmConstant=psi_transition_KM,
linkedReaction=lrxn,
onThr=0.,
offset=psi0),
True,
isODE=True)
# Need a copy to avoid duplicate entries; TODO: add option for class model to not update object dMetabolite/dReaction
#biomass_psi_transition_ECac = copy.copy(biomass_psi_transition_ECgl)
biomass_psi_transition_ECac = cre.DReaction(
None,
cre.MichaelisMentenLinked(ex_acetate_ECgl,
lowerBound=0.,
maxVelocity=psi_transition_rate,
hillCoeff=hctrans,
mmConstant=psi_transition_KM,
linkedReaction=lrxn,
onThr=0.,
offset=psi0),
True,
isODE=True)
## phi = Ac -> Glc ##
## ECac senses Glc and transitions
biomass_phi_transition_ECac = cre.DReaction(
None,
cre.MichaelisMentenLinked(ex_glucose_ECac,
lowerBound=0.,
maxVelocity=phi_transition_rate,
hillCoeff=hctrans,
mmConstant=phi_transition_KM,
linkedReaction=lrxn2,
onThr=0.,
offset=phi0),
True,
isODE=True)
# Need a copy to avoid duplicate entries; TODO: add option for class model to not update object dMetabolite/dReaction
#biomass_phi_transition_ECgl = copy.copy(biomass_phi_transition_ECac)
biomass_phi_transition_ECgl = cre.DReaction(
None,
cre.MichaelisMentenLinked(ex_glucose_ECac,
lowerBound=0.,
maxVelocity=phi_transition_rate,
hillCoeff=hctrans,
mmConstant=phi_transition_KM,
linkedReaction=lrxn2,
onThr=0.,
offset=phi0),
True,
isODE=True)
#acetate_fed_ECgl = cre.DReaction(None, cre.ConcentrationMaintenanceFunction(ex_acetate_ECgl, ac_thr, t_glc, linkedReactions=[acetate_exchange_ECgl]), True, isODE=True)
acetate_fed_ECgl = cre.DReaction(None,
cre.SquareWave(min(9.1, vmaxexace), 20, 1,
t_glc),
True,
isODE=True)
dyRxn_ECgl = {
'glucose_exchange': glucose_exchange_ECgl,
'oxygen_exchange': oxygen_exchange_ECgl,
'acetate_fed': acetate_fed_ECgl,
'acetate_exchange': acetate_exchange_ECgl,
'psi_transition': biomass_psi_transition_ECgl,
'phi_transition': biomass_phi_transition_ECgl,
'growth': growth_ECgl
}
dyMet_ECgl = {
'biomass_ECgl': biomass_ECgl,
'ex_glucose': ex_glucose_ECgl,
'ex_acetate': ex_acetate_ECgl
}
model_ECgl = cmo.DynamicModel(dyRxn_ECgl,
dyMet_ECgl,
mpath,
volExt,
volUn,
'optlang-glpk',
exitname + '_ECgl',
savePath=args.pathout)
dyRxn_ECac = {
'glucose_exchange': glucose_exchange_ECac,
'oxygen_exchange': oxygen_exchange_ECac,
'acetate_fed': acetate_fed_ECac,
'acetate_exchange': acetate_exchange_ECac,
'psi_transition': biomass_psi_transition_ECac,
'phi_transition': biomass_phi_transition_ECac,
'growth': growth_ECac
}
dyMet_ECac = {
'biomass_ECac': biomass_ECac,
'ex_glucose': ex_glucose_ECac,
'ex_acetate': ex_acetate_ECac
}
model_ECac = cmo.DynamicModel(dyRxn_ECac,
dyMet_ECac,
mpath,
volExt,
volUn,
'optlang-glpk',
exitname + '_ECac',
savePath=args.pathout)
model_ECgl.loadSBMLModel()
model_ECgl.resetObjective()
model_ECgl.setObjective(growth_ECgl.modName, 1.)
model_ECgl.constrainSources(element='C', count=0, uptakeOff=True)
model_ECgl.initializeConcentrations()
model_ECgl.setParsimoniousFBA(True)
model_ECgl.setMinimalMaintenance(growth_ECgl.modName, -0.15)
model_ECgl.setQuitOnFBAError(False)
model_ECgl.setBoundsThreshold(0.8 * vmaxexglc)
model_ECgl.cbModel.reactions.get_by_id('ATPM').upper_bound = 8.39
model_ECgl.cbModel.reactions.get_by_id('ATPM').lower_bound = 8.39
model_ECac.loadSBMLModel()
model_ECac.resetObjective()
model_ECac.setObjective(growth_ECac.modName, 1.)
model_ECac.constrainSources(element='C', count=0, uptakeOff=True)
model_ECac.initializeConcentrations()
model_ECac.setParsimoniousFBA(True)
model_ECac.setMinimalMaintenance(growth_ECac.modName, -0.15)
model_ECac.setQuitOnFBAError(False)
model_ECac.setBoundsThreshold(0.8 * vmaxexace)
model_ECac.cbModel.reactions.get_by_id('ATPM').upper_bound = 8.39
model_ECac.cbModel.reactions.get_by_id('ATPM').lower_bound = 8.39
if args.moma:
model_ECgl.setMOMA(True)
model_ECac.setMOMA(True)
model_comm = cco.Consortium({
'ECgl': model_ECgl,
'ECac': model_ECac
}, {
'biomass_ECgl': ['ECgl'],
'biomass_ECac': ['ECac'],
'ex_glucose': ['ECgl', 'ECac'],
'ex_acetate': ['ECgl', 'ECac']
},
'optlang-glpk',
name=exitname + '_ECgl_ECac_' + strratioecgl,
savePath=args.pathout)
print('<<<>>> Simulation parameters: ')
print('<<<>>> Run type: consortium | single | ecgl | ecac ')
print('<<<>>> \t %d | %d | %d | %d ' %
(args.runconsortium, args.runsingle, args.runecgl, args.runecac))
print(
'<<<>>> Exp condition: high acetate | low acetate | mixed acetate | fed low acetate | fed high acetate'
)
print('<<<>>> \t %d | %d | %d | %d | %d ' %
(args.runhighacetate, args.runlowacetate, args.runmixedacetate,
args.runfedlowacetate, args.runfedhighacetate))
print('<<<>>> BM0 \t ECgl \t ECac \t Glc0 \t Ac0 \t death rate')
print('<<<>>> %.3f \t %.3f \t %.3f \t %.3f \t %.3f \t %.3f ' %
(biomass0, pcECgl, (1 - pcECgl), glucose0, acetate0, death_rate))
print('<<<>>> VMGlc \t KMGlc \t VMAc \t KMAc \t XI \t ZETA')
print('<<<>>> %.3f \t %.3f \t %.3f \t %.3f \t %.3f \t %.3f ' %
(vmaxexglc, kmuptake, vmaxexace, kmuptake, fb, afbA))
print('<<<>>> PSI transition %d ' % args.psitransition)
print('<<<>>> Psi0 \t VMpsi \t KMpsi \t epsilonpsi')
print(
'<<<>>> %.3f \t %.3f \t %.3f \t %.3f ' %
(psi0, psi_transition_rate, psi_transition_KM, transition_efficiency))
print('<<<>>> PHI transition %d ' % args.phitransition)
print('<<<>>> Phi0 \t VMphi \t KMphi \t epsilonphi')
print(
'<<<>>> %.3f \t %.3f \t %.3f \t %.3f ' %
(phi0, phi_transition_rate, phi_transition_KM, transition_efficiency))
if verbose:
for m in model_comm.dmodelsKeys:
for r in model_comm.dmodels[m].dreactionsKeys:
print(m, r)
attrs = vars(model_comm.dmodels[m].dreactions[r].kinF)
print ', '.join("%s: %s" % item for item in attrs.items())
if not args.run:
return model_comm
if args.runconsortium:
model_comm.runConsortiumDynamicFBA(
maxtime,
'vode', (0, float(args.minstep), 1., int(args.nsteps)),
verbose=False)
elif args.runecgl:
model_ECgl.runDynamicFBA(
maxtime,
'vode', (0, float(args.minstep), 1., int(args.nsteps)),
False,
quitOnFBAError=False)
elif args.runecac:
model_ECac.runDynamicFBA(
maxtime,
'vode', (0, float(args.minstep), 1., int(args.nsteps)),
False,
quitOnFBAError=False)
return
def main():
'''
Test dynamicModel on a pure ODE system
'''
opts, args = options()
verbose = opts.verbose
timeunits = opts.timeunits
outpath = opts.pathout
volExt = 1
volUn = 'mL'
exitname = "lotkavolterra"
#parameters = {'a': 1., 'b': 0.1, 'c': 1.5, 'd': 0.75}
parameters = {
'a': float(opts.apar),
'b': float(opts.bpar),
'c': float(opts.cpar),
'd': float(opts.dpar)
}
y0 = eval(opts.yicond)
frabbit = 'self.p["a"]*RABBITS - self.p["b"]*RABBITS*FOXES'
ffoxes = '-self.p["c"]*FOXES + self.p["d"]*self.p["b"]*RABBITS*FOXES'
#ODE system
r_biomass = cme.Biomass(
[y0[0]], {
'r_growth': [(1, 'r_biomass')],
'r_death': [(-1, 'r_biomass', 'f_biomass')]
})
f_biomass = cme.Biomass(
[y0[1]], {
'f_death': [(-1, 'f_biomass')],
'f_growth': [(1, 'r_biomass', 'f_biomass')]
})
r_growth = cre.DReaction('r_growth',
cre.FixedBound(0., parameters['a']),
isODE=True)
r_death = cre.DReaction('r_death',
cre.FixedBound(0., parameters['b']),
isODE=True)
f_growth = cre.DReaction('f_growth',
cre.FixedBound(
0., (parameters['d'] * parameters['b'])),
isODE=True)
f_death = cre.DReaction('f_death',
cre.FixedBound(0., parameters['c']),
isODE=True)
dyRxn = {
'r_growth': r_growth,
'r_death': r_death,
'f_growth': f_growth,
'f_death': f_death
}
dyMet = {'r_biomass': r_biomass, 'f_biomass': f_biomass}
model = cmo.DynamicModel(dyRxn,
dyMet,
None,
volExt,
volUn,
'optlang-glpk',
exitname,
timeU=timeunits,
savePath=outpath)
model.initializeConcentrations()
model.runDynamicFBA(18, 'vode', (0, 0., 1., 1000), False)
mname1 = exitname.replace(' ', '_')
mname = mname1.split('_')[0]
x = np.array(model.T)
bmf = np.array(model.dmetabolites['f_biomass'].quantity)
bmr = np.array(model.dmetabolites['r_biomass'].quantity)
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot(x, bmf, 'b-', label='Foxes')
ax1.plot(x, bmr, 'r-', label='Rabbits')
ax1.set_ylabel('Population size')
ax1.set_xlim(0, 18)
ax1.grid()
ll = ax1.legend(loc='best', prop={'size': 8})
fig1.savefig('%s/%s.png' % (outpath, mname1))
return