def reac_ko_medium_optim_CM(isMultiProc=False, size=(1, 1), withCobraPy=False):
SBML_FILE = basePath + "Data/EC_SC_model.xml"
modelaux = load_cbmodel(SBML_FILE, exchange_detection_mode="R_EX_")
model = fix_exchange_reactions_model(modelaux)
fileRes = basePath + "Results/optim_Comunity_KO_medium_pFBA.csv"
for r_id, rxn in model.reactions.items():
if r_id.startswith(
'R_EX_'): # ou tambem podes fazer if rxn.is_exchange:
rxn.lb = -1000 if rxn.lb is None else rxn.lb
rxn.ub = 1000 if rxn.ub is None else rxn.ub
simulProb = StoicSimulationProblem(model,
objective={"R_BM_total_Synth": 1},
withCobraPy=withCobraPy)
res = simulProb.simulate()
print(res.print())
evalFunc = build_evaluation_function("BP_MinModifications",
"R_BM_total_Synth",
"R_EX_succ_medium_")
result = cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM_REACTION_KO,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def prot_ko_optim(prot_measure_fractions=None,
prot_measure_ggdw=None,
constraints=None,
isMultiProc=False,
size=1):
#load model
if prot_measure_fractions is None and prot_measure_ggdw is None:
model = GeckoModel("single-pool")
else:
model = GeckoModel("multi-pool")
if prot_measure_fractions:
model.limit_proteins(fractions=prot_measure_fractions)
else:
model.limit_proteins(ggdw=prot_measure_ggdw)
fileRes = basePath + "Results/optim_KO_Gecko_Yeast_SUCC_max5.csv"
simulProb = GeckoSimulationProblem(model, constraints=constraints)
evalFunc = build_evaluation_function("BPCY", "r_2111", "r_2056",
"r_1714_REV") # max succ exchange
gecko_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes) #KO_Reaction by default
def jahan_ko():
simulProblem, idsToManipulate = jahan_model()
decoder = DecoderReacKnockouts(idsToManipulate)
evalFunc = build_evaluation_function("targetFlux", ["vD_SUC"])
solutions = [[
'CS', 'Pps', 'MS', 'MDH', 'Pfk', 'vTktB_max', 'vAck_max', 'v6PGDH_max',
'Ppc'
],
[
'CS', 'Pps', 'Icl', 'MS', 'MDH', 'Pfk', 'vTktB_max',
'vAck_max', 'v6PGDH_max', 'Ppc'
], ['CS', 'MDH', 'aKGDH', 'vPta_max', 'Ppc'],
['CS', 'Pps', 'MS', 'MDH', 'vPta_max', 'Ppc'],
['CS', 'Pps', 'Icl', 'MS', 'MDH', 'vPta_max', 'Ppc'],
['CS', 'Pps', 'Icl', 'MS', 'MDH', 'vPta_max', 'Ppc', 'Fbp'],
['CS', 'MDH', 'vPta_max', 'Ppc', 'Pps'],
['CS', 'Pps', 'MDH', 'vAck_max', 'v6PGDH_max', 'Ppc'],
['vPgi_max', 'vPta_max', 'Ppc'],
['CS', 'MDH', 'vPta_max', 'Ppc'],
['CS', 'MDH', 'vPta_max', 'Ppc', 'Pps'],
['vPgi_max', 'vPta_max', 'Ppc'],
['CS', 'MDH', 'vPta_max', 'Ppc'],
['vPgi_max', 'vPta_max', 'Ppc'], ['vAck_max', 'Ppc'],
['vPTS4_max']]
results = simulate_solutions(simulProblem, decoder, solutions)
yield_solutions(results, "vD_SUC", "vD_GLCfeed")
def ko_millard(isMultiProc=False, size=1):
EAConfigurations.MAX_CANDIDATE_SIZE = size
sbmlFile = '../../../examples/models/Millard2016v2.xml'
fileRes = basePath + "Results/optim_Millard_acet_ko_" + str(size) + ".csv"
fileLastGen = basePath + "Results/optim_Millard_acet_ko_" + str(
size) + "_lastgen.csv"
mapParamReacs = OrderedDict([
('PTS_4', ['eiicbP']), ('PTS_0', ['ei']), ('PTS_1', ['eiP']),
('PTS_2', ['eiia']), ('PTS_3', ['eiicb']), ('PGI', ['PGI_Vmax']),
('PFK', ['PFK_Vmax']), ('FBA', ['FBA_Vmax']), ('TPI', ['TPI_Vmax']),
('GDH', ['GDH_Vmax']), ('PGK', ['PGK_Vmax']), ('GPM', ['GPM_Vmax']),
('ENO', ['ENO_Vmax']), ('PYK', ['PYK_Vmax']), ('ZWF', ['ZWF_Vmax']),
('PGL', ['PGL_Vmax']), ('GND', ['GND_Vmax']), ('RPE', ['RPE_Vmax']),
('RPI', ['RPI_Vmax']), ('X5P_GAP_TKT', ['tkt']),
('F6P_E4P_TKT', ['tktC2']), ('S7P_R5P_TKT', ['tktC2']),
('F6P_GAP_TAL', ['talC3']), ('S7P_E4P_TAL', ['tal']),
('FBP', ['FBP_Vmax']), ('PPC', ['PPC_Vmax']), ('PCK', ['PCK_Vmax']),
('PPS', ['PPS_Vmax']), ('MAD', ['MAD_Vmax']), ('PDH', ['PDH_Vmax']),
('GLT', ['GLT_Vmax']), ('ACN_1', ['ACN_1_Vmax']),
('ACN_2', ['ACN_2_Vmax']), ('ICD', ['icd']), ('LPD', ['LPD_Vmax']),
('SK', ['SK_Vmax']), ('SDH', ['SDH_Vmax']), ('FUMA', ['FUMA_Vmax']),
('MQO', ['MQO_Vmax']), ('MDH', ['MDH_Vmax']), ('ACEA', ['ACEA_Vmax']),
('ACEB', ['ACEB_Vmax']), ('EDD', ['EDD_Vmax']), ('EDA', ['EDA_Vmax']),
('NADH_req', ['NADH_req_Vmax']), ('ATP_syn', ['ATP_syn_Vmax']),
('ACK', ['ACK_Vmax']), ('ACS', ['ACS_Vmax']), ('PTA', ['PTA_Vmax']),
('MYTBO', ['MYTBO_Vmax']), ('SQR', ['SQR_Vmax']),
('NDHII', ['NDHII_Vmax']), ('GROWTH', ['GROWTH_Vmax']),
('ATP_MAINTENANCE', ['ATP_MAINTENANCE_Vmax']),
('XCH_GLC', ['XCH_GLC_Vmax']), ('PIT', ['PIT_Vmax']),
('XCH_P', ['XCH_P_Vmax']), ('XCH_ACE1', ['XCH_ACE1_Vmax']),
('XCH_ACE2', ['XCH_ACE2_Vmax'])
])
model = load_kinetic_model(sbmlFile, mapParamReacs)
objFunc = build_evaluation_function("targetFlux", ["_ACE_OUT"])
simulProblem = KineticSimulationProblem(
model, tSteps=[0, KineticConfigurations.STEADY_STATE_TIME])
result = kinetic_strain_optim(simulProblem,
objFunc=objFunc,
levels=None,
criticalParameters=[
'ATP_MAINTENANCE_Vmax', 'GROWTH_Vmax',
'NDHII_Vmax', 'PIT_Vmax', 'eiicbP', 'ei',
'eiP', 'eiia'
],
isMultiProc=isMultiProc,
resultFile=fileRes,
initPopFile=fileLastGen)
result.print()
def medium_Lactate_optim(isMultiProc=False, size=1, withCobraPy=False):
SBML_FILE = "../../../examples/models/Ec_iAF1260.xml"
modelaux = load_cbmodel(SBML_FILE, flavor="cobra")
model = fix_exchange_reactions_model(modelaux)
fileRes = basePath + "Results/optim_Ec_iAF1260_medium_lactate.csv"
simulProb = StoicSimulationProblem(
model,
objective={"R_Ec_biomass_iAF1260_core_59p81M": 1},
withCobraPy=withCobraPy)
fba = simulProb.simulate()
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
criticalReacs = essential
# set the minimum production of biomass
simulProb.set_objective_function({"R_EX_lac_L_e": 1})
simulProb.constraints.update({
"R_Ec_biomass_iAF1260_core_59p81M":
(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.75, 9999)
})
minObjective = {"R_EX_lac_L_e": 1e-5}
print(
"Biomass: " +
str(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.75))
build_evaluation_function
evalFunc = build_evaluation_function("MinNumberReac", size, minObjective)
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM,
criticalReacs=criticalReacs,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def framed_optim(isMultiProc, size):
model = load_cbmodel(SBML_FILE, flavor="cobra")
fileRes = basePath + "Results/optim_Ec_iAF1260_ko.csv"
simulProb = StoicSimulationProblem(
model, objective={"R_Ec_biomass_iAF1260_core_59p81M": 1})
objFunc = build_evaluation_function("targetFlux",
["R_Ec_biomass_iAF1260_core_59p81M"])
cbm_strain_optim(simulProb,
evaluationFunc=objFunc,
levels=None,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def medium_SC_Etanol_Levels_optim(isMultiProc=False,
size=1,
withCobraPy=False):
SBML_FILE = "../../../examples/models/iMM904.xml"
model = load_cbmodel(SBML_FILE, flavor="fbc2")
fileRes = basePath + "Results/optim_iMM904_etanol_levels.csv"
simulProb = StoicSimulationProblem(model,
objective={"R_BIOMASS_SC5_notrace": 1},
withCobraPy=withCobraPy)
fba = simulProb.simulate()
print("Biomass: " +
str(fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"]))
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
criticalReacs = essential
# set the minimum production of biomass
simulProb.set_objective_function({"R_EX_etoh_e": 1})
simulProb.constraints.update({
"R_BIOMASS_SC5_notrace":
(fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"] * 0.75, 9999)
})
print("Biomass: " +
str(fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"] * 0.75))
evalFunc = build_evaluation_function(
"MinNumberReacAndMaxFluxWithLevels", size, LEVELS,
{"R_EX_etoh_e": model.reactions["R_EX_etoh_e"].ub})
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=LEVELS,
type=optimType.MEDIUM_LEVELS,
criticalReacs=criticalReacs,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def medium_reac_ko_optim(isMultiProc=False, size=[5, 5], withCobraPy=False):
SBML_FILE = "../../../examples/models/Ec_iAF1260.xml"
model = load_cbmodel(SBML_FILE, flavor="cobra")
newModel = fix_exchange_reactions_model(model)
fileRes = basePath + "Results/optim_Ec_iAF1260_medium_ko_succ.csv"
simulProb = StoicSimulationProblem(
newModel,
objective={"R_Ec_biomass_iAF1260_core_59p81M": 1},
withCobraPy=withCobraPy)
# set the minimum production of biomass
fba = simulProb.simulate()
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
simulProb.set_objective_function({"R_EX_succ_e_": 1})
simulProb.constraints.update({
"R_Ec_biomass_iAF1260_core_59p81M":
(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.25, 9999)
})
print(
"Biomass: " +
str(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.25))
evalFunc = build_evaluation_function(
"MinNumberReacAndMaxFlux", sum(size),
{"R_EX_succ_e": model.reactions["R_EX_succ_e"].ub})
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM_REACTION_KO,
criticalReacs=essential,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes) # KO_Reaction by default
def under_over_chassagnole(isMultiProc=False, size=1):
sbmlFile = '../../../examples/models/chassagnole2002.xml'
fileRes = basePath + "Results/optim_Chassagnole_Serine_underover_" + size + ".csv"
model = load_kinetic_model(sbmlFile)
objFunc = build_evaluation_function("targetFlux", ["vsersynth"])
simulProblem = KineticSimulationProblem(
model, tSteps=[0, KineticConfigurations.STEADY_STATE_TIME])
result = kinetic_strain_optim(simulProblem,
objFunc=objFunc,
levels=LEVELS,
criticalParameters=[],
isMultiProc=False,
candidateSize=size,
resultFile=fileRes)
result.print()
def ko_chassagnole(isMultiProc=False, size=1):
sbmlFile = '../../../examples/models/chassagnole2002.xml'
fileRes = basePath + "Chassagnole/optim_Chassagnole_Serine_ko_" + str(
size) + ".csv"
model = load_kinetic_model(sbmlFile)
objFunc = build_evaluation_function("targetFlux", ["vsersynth"])
simulProblem = KineticSimulationProblem(
model, tSteps=[0, KineticConfigurations.STEADY_STATE_TIME])
result = kinetic_strain_optim(simulProblem,
objFunc=objFunc,
levels=None,
type=optimType.REACTION_KO,
isMultiProc=True,
candidateSize=size,
resultFile=fileRes)
result.print()
def cobra_optim(isMultiProc, size):
model = read_sbml_model(SBML_FILE)
fileRes = basePath + "Results/optim_Ec_iAF1260_ko_cobra.csv"
simulProb = StoicSimulationProblem(
model,
objective={"Ec_biomass_iAF1260_core_59p81M": 1},
withCobraPy=True)
objFunc = build_evaluation_function("targetFlux",
["Ec_biomass_iAF1260_core_59p81M"])
print(simulProb.simulate())
cbm_strain_optim(simulProb,
evaluationFunc=objFunc,
levels=None,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def medium_SC_optim(isMultiProc=False, size=1, withCobraPy=False):
SBML_FILE = "../../../examples/models/iMM904.xml"
model = load_cbmodel(SBML_FILE, flavor="fbc2")
fileRes = basePath + "Results/optim_iMM904_medium.csv"
simulProb = StoicSimulationProblem(model,
objective={"R_BIOMASS_SC5_notrace": 1},
withCobraPy=withCobraPy)
fba = simulProb.simulate()
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
criticalReacs = essential
# set the minimum production of biomass
minObjective = {
"R_BIOMASS_SC5_notrace":
fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"] * 0.75
}
evalFunc = build_evaluation_function("MinNumberReac", size, minObjective)
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM,
criticalReacs=criticalReacs,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def jahan_uo():
simulProblem, idsToManipulate = jahan_model()
levels = [
0, 2**-5, 2**-4, 2**-3, 2**-2, 2**-1, 2**1, 2**2, 2**3, 2**4, 2**5
]
decoder = DecoderReacUnderOverExpression(idsToManipulate, levels)
evalFunc = build_evaluation_function("targetFlux", ["vD_SUC"])
solutions = [
# OrderedDict([('CS', 32), ('PDH', 32), ('Fbp', 32), ('MS', 16), ('ICDH', 0), ('vNonPTS_max', 32), ('SDH', 0.03125),('v6PGDH_max', 0), ('vPTS4_max', 32), ('vAck_max', 0)]),
# OrderedDict([('SDH', 0.0625), ('Pyk', 0), ('vG6PDH_max', 0.03125), ('vPta_max', 0), ('CS', 8), ('Fbp', 4), ('Ppc', 32),('vPTS4_max', 32)]),
# OrderedDict([('Pyk', 0), ('vG6PDH_max', 0.03125), ('vPta_max', 0), ('Fbp', 4), ('SDH', 0.0625), ('CS', 8),('vPTS4_max', 32), ('Ppc', 32)]),
#OrderedDict([('vG6PDH_max', 0), ('vPta_max', 0), ('MS', 16), ('ICDH', 0), ('SDH', 0.0625), ('vPTS4_max', 32)]),
#OrderedDict([('SDH', 0.125), ('vG6PDH_max', 0.03125), ('vPTS4_max', 32), ('ICDH', 0), ('vAck_max', 0)])
{
'Ppc': 4,
'v6PGDH_max': 16,
'Fbp': 4,
'Pps': 16,
'Pyk': 0.03125,
'vPta_max': 0,
'SDH': 0.25,
'vPTS4_max': 16
}
#OrderedDict([('v6PGDH_max', 0.0625), ('SDH', 0.03125), ('vAck_max', 0), ('vPTS4_max', 32), ('ICDH', 0)]),
#OrderedDict([('v6PGDH_max', 0.0625), ('SDH', 0.03125), ('vAck_max', 0), ('vPTS4_max', 32)]),
#OrderedDict([('v6PGDH_max', 0.0625), ('SDH', 0.03125), ('vAck_max', 0), ('ICDH', 0)]),
#OrderedDict([('v6PGDH_max', 0.0625), ('SDH', 0.03125), ('vPTS4_max', 32), ('ICDH', 0)]),
#OrderedDict([('v6PGDH_max', 0.0625), ('vAck_max', 0), ('vPTS4_max', 32), ('ICDH', 0)]),
#OrderedDict([ ('SDH', 0.03125), ('vAck_max', 0), ('vPTS4_max', 32), ('ICDH', 0)]),
# OrderedDict([('ICDH', 0.125), ('vPta_max', 0), ('SDH', 0.25), ('vPTS4_max', 32)]),
#OrderedDict([('vPta_max', 0), ('ICDH', 0.125), ('SDH', 0.25), ('vPTS4_max', 32)]),
# OrderedDict([('ICDH', 0.125), ('SDH', 0.03125), ('vAck_max', 0)]),
# OrderedDict([('SDH', 0.03125), ('Pps', 32)]),
# OrderedDict([('Pps', 32)])
]
#results = simulate_solutions(simulProblem, decoder, [])
#yield_solutions(results, "vD_SUC", "vD_GLCfeed")
print("WITH UO")
results = simulate_solutions(simulProblem, decoder, solutions)
yield_solutions(results, "vD_SUC", "vD_GLCfeed")
def reac_ko_optim(isMultiProc=False, size=1, withCobraPy=False):
SBML_FILE = "../../../examples/models/Ec_iAF1260.xml"
if withCobraPy:
model = read_sbml_model(SBML_FILE)
fileRes = basePath + "Results/optim_Ec_iAF1260_ko_cobra.csv"
else:
model = load_cbmodel(SBML_FILE, flavor="cobra")
fileRes = basePath + "Results/optim_Ec_iAF1260_ko.csv"
simulProb = StoicSimulationProblem(
model,
objective={"Ec_biomass_iAF1260_core_59p81M": 1},
withCobraPy=withCobraPy)
evalFunc = build_evaluation_function("targetFlux",
["Ec_biomass_iAF1260_core_59p81M"])
result = cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes) # KO_Reaction by default
result.print()
if __name__ == "__main__":
# First Step
# Load the Model: Cobra or Framed
optimmodels_path = os.path.dirname(optimModels.__file__)
ecoli_model = os.path.join(optimmodels_path, "examples", "models",
"Ec_iAF1260.xml") # path to the model file
framed_model = load_cbmodel(filename=ecoli_model, flavor="cobra")
# Second Step
# Evaluating Function # different functions require different arguments
ec_stoic_biomass = "R_Ec_biomass_iAF1260_core_59p81M"
ec_stoic_succinate = "R_EX_succ_e"
stoic_eval_function = build_evaluation_function(
"WYIELD", # evaluating function id
ec_stoic_biomass, # biomass reaction id
ec_stoic_succinate, # target reaction id
alpha=0.3, # percentage of maximum target considered
minBiomassValue=0.03135 # minimum biomass for viable solution
)
# Third Step
# Run Optimization
result = stoic_optimization(
model=framed_model,
optim_type="KO", # "KO", "UO", "MEDKO", "MEDUO", "KO+MEDKO"
eval_func=stoic_eval_function,
constraints={}, # contraints format: {"reac_id": (lb, up), ...}
critical_proteins="auto", # can also use a list of reactions
isMultiProc=False, # replace with True to use multi-processing
size=10, # chose max number of alterations, None uses default
output_file="optimization_results.csv" # chose path of the results file
logger.debug(
'completed parallel_evaluation_mp in {0} seconds'.format(end -
start))
# print("--- %s seconds ---" % (time.time() - start), 'end_pop')
return [r.get()[0] for r in results]
model = GeckoModel('single-pool')
const = {'r_1714_REV': (0, 1)}
simulProblem = GeckoSimulationProblem(model, const)
ids = [
x for x in simulProblem.model.proteins
if x not in simulProblem.objective.keys()
]
decoder = DecoderProtKnockouts(ids)
evalFunc = build_evaluation_function("targetFlux", ["r_2056"])
eaConfig = EAConfigurations()
optimProbConf = OptimProblemConfiguration(
simulProblem,
type=optimType.PROTEIN_KO,
decoder=decoder,
evaluationFunc=evalFunc,
EAConfig=eaConfig,
scaleProblem=GeckoConfigurations.SCALE_CONSTANT)
candidates = [{321, 51}, {65, 130, 259, 146, 660, 91},
{488, 266, 235, 558, 119, 220}, {81, 431}, {160, 425, 162, 606},
{97, 330, 654, 212, 671}, {419, 589, 398, 302, 275, 214},
{64, 648, 275, 221, 574}, {11, 308, 21, 614}, {18, 77, 222},
{426, 60}, {110, 667, 358}, {290, 387, 362, 119, 223},
def ko_jahan():
sbmlFile = '/Volumes/Data/Documents/Projects/DeCaF/Optimizations/Data/Jahan2016_chemostat_fixed.xml'
fileRes = "/Volumes/Data/Documents/Projects/DeCaF/Optimizations/Results/optim_Jahan_Suc_ko.csv"
mapParamReacs = {
"vE_6PGDH": ["v6PGDH_max"],
"vE_Ack": ["vAck_max"],
"vE_Ack_medium": ["vAck_max"],
"vE_Cya": ["vCya_max"],
"vE_Eda": ["vEda_max"],
"vE_Edd": ["vEdd_max"],
"vE_Fum": ["Fum"],
"vE_G6PDH": ["vG6PDH_max"],
"vE_MDH": ["MDH"],
"vE_Pgi": ["vPgi_max"],
"vE_Pgl": ["vPgl_max"],
"vE_Pta": ["vPta_max"],
"vE_R5PI": ["vR5PI_max"],
"vE_Ru5P": ["vRu5P_max"],
"vE_Tal": ["vTal_max"],
"vE_TktA": ["vTktA_max"],
"vE_TktB": ["vTktB_max"],
"vE_cAMPdegr": ["vcAMPdegr_max"],
"vNonPTS": ["vNonPTS_max"],
"vNonPTS_medium": ["vNonPTS_max"],
"vPTS4": ["vPTS4_max"],
"vPTS4_medium": ["vPTS4_max"],
"vE_AceKki": ["AceK"],
"vE_AceKph": ["AceK"],
"vE_Acs": ["Acs"],
"vE_Acs_medium": ["Acs"],
"vE_CS": ["CS"],
"vE_Fba": ["Fba"],
"vE_Fbp": ["Fbp"],
"vE_GAPDH": ["GAPDH"],
"vE_Glk": ["Glk"],
"vE_ICDH": ["ICDH"],
"vE_Icl": ["Icl"],
"vE_MS": ["MS"],
"vE_Mez": ["Mez"],
"vE_PDH": ["PDH"],
"vE_Pck": ["Pck"],
"vE_Pfk": ["Pfk"],
"vE_Ppc": ["Ppc"],
"vE_Pps": ["Pps"],
"vE_Pyk": ["Pyk"],
"vE_SDH": ["SDH"],
"vE_aKGDH": ["aKGDH"]
}
model = load_kinetic_model(sbmlFile, mapParamReacs)
objFunc = build_evaluation_function("targetFlux", ["vD_SUC"])
simulProblem = KineticSimulationProblem(
model, tSteps=[0, KineticConfigurations.STEADY_STATE_TIME])
result = kinetic_strain_optim(simulProblem,
objFunc=objFunc,
levels=LEVELS,
isMultiProc=False,
candidateSize=size,
resultFile=fileRes)
result.print()
if __name__ == "__main__":
# First Step:
# Loading the model
optimmodels_path = os.path.dirname(optimModels.__file__)
example_chassagnole_path = os.path.join(optimmodels_path, "examples", "models", "chassagnole2002.xml")
example_chassagnole_kmap = build_chassaganole_map()
ex_kinetic_model = load_kinetic_model(
filename = example_chassagnole_path,
kmap = OrderedDict(example_chassagnole_kmap)
)
# Second Step
# Evaluation Function
example_evaluation = build_evaluation_function("targetFlux", ["vPK"])
# Third Step
# Optimization
results = kinetic_optimization(
model = ex_kinetic_model,
optim_type = "KO", # KO or KO
eval_func = example_evaluation,
critical_parameters = [], # list of paramters ids str to avoid in optmization
isMultiProc = False, # replace with True to use multi-processing
size = 10, # chose max number of alterations, None uses default
output_file = "optimization_results.csv"
)
for result in results:
result.print()