def kinetic_simulation(model, parameters=None, factors=None, time=1e9):
"""
Runs a phenotype simulation using dynamic models.
Args:
model (kineticModel): The kinetic metabolic model.
parameters (dict): List of parameters that will be set with new values (ex: Dilution, initial concentrations).
factors (dict): Values to by multiplied to the vMax parameters (KO: the value should be 0, Under: value between 0 and 1,
Over: value higher than 1)
time (float): End time for steady-state.
Returns (kineticSimulationResults): The function returns the best solutions found in strain optimization. The kineticSimulationResults have the
flux distribution and metabolites concentration on steady-state, and the modifications made over the
original model.
"""
simulProblem = KineticSimulationProblem(model,
parameters=parameters,
tSteps=[0, time],
timeout=None)
if factors:
override = OverrideKineticSimulProblem(factors=factors)
result = simulProblem.simulate(override)
else:
result = simulProblem.simulate()
return result
def kinetic_simulation(kinet_model, factors, **kwargs):
"""
default template function to load models
:param load_kinetic_model() kinet_model:
:param dict factors:
:return: simulation result
"""
time = kwargs.get("time", KineticConfigurations.STEADY_STATE_TIME)
override = OverrideKineticSimulProblem(factors=factors)
simulProblem = KineticSimulationProblem(kinet_model, tSteps=[0, time])
res = simulProblem.simulate(overrideSimulProblem=override)
return res
def kinetic_optimization(model, optim_type, eval_func, **kwargs):
"""
default template function for kinetic model optimization
:param load_kinetic_model() model: kinetic model object
:param str optim_type: "KO" - Knockouts or "UO" - Under/Over expression
:param build_evaluation_function() eval_func: evaluating function
:param kwargs: all of the optional arguments
:return kinetic_strain_optim(): result of the optimization
"""
output_file = kwargs.get("output_file", "results.csv")
time = kwargs.get("time", KineticConfigurations.STEADY_STATE_TIME)
cand_size = kwargs.get("size", None)
multi_thread = kwargs.get("isMultiProc", False)
crit_params = kwargs.get("critical_parameters", [])
kinetic_uo_levels = kwargs.get(
"levels", [2 ** -5, 2 ** -4, 2 ** -3, 2 ** -2, 2 ** -1, 2 ** 1, 2 ** 2, 2 ** 3, 2 ** 4, 2 ** 5])
if optim_type == "KO":
optim_type = optimType.REACTION_KO
elif optim_type == "UO":
optim_type = optimType.REACTION_UO
kinetic_simul_problem = KineticSimulationProblem(model, tSteps=[0, time])
res = kinetic_strain_optim(
simulProblem = kinetic_simul_problem,
objFunc = eval_func,
levels = kinetic_uo_levels,
type = optim_type,
isMultiProc = multi_thread,
candidateSize = cand_size,
criticalParameters = crit_params,
resultFile = output_file)
return res
def jahan_model():
sbmlFile = '../../../examples/models/Jahan2016_chemostat_fixed.xml'
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)
simulProblem = KineticSimulationProblem(
model, tSteps=[0, KineticConfigurations.STEADY_STATE_TIME], timeout=60)
idsToManipulate = list(
set(
itertools.chain.from_iterable(
simulProblem.model.reacParamsFactors.values())))
return simulProblem, idsToManipulate
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 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 millard_model():
sbmlFile = '../../../examples/models/Millard2016_suc_drain.xml'
mapParamReacs = {
'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)
simulProblem = KineticSimulationProblem(
model,
tSteps=[0, KineticConfigurations.STEADY_STATE_TIME],
timeout=600)
critParams = [
'ATP_MAINTENANCE_Vmax', 'GROWTH_Vmax', 'NDHII_Vmax', 'PIT_Vmax',
'eiicbP', 'ei', 'eiP', 'eiia'
]
allParams = list(
set(
itertools.chain.from_iterable(
simulProblem.model.reacParamsFactors.values())))
idsToManipulate = [x for x in allParams if x not in critParams]
return simulProblem, idsToManipulate
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()