def load_yeast():
"""Loads a configuration for optimizations on yeast model iMM904SL_v6,
including the model, environmental conditions, non targets for modifications,
the biomass equation ID, and wild type reference flux values.
Returns: A dictionary constaining the configuration.
"""
DIR = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(DIR, '../models/yeast/')
DATA_FILE = os.path.join(PATH, "iMM904SL_v6.xml")
BIOMASS_ID = 'R_biomass_SC5_notrace'
O2 = 'R_EX_o2_e_'
GLC = 'R_EX_glc_e_'
model = load_cbmodel(DATA_FILE, flavor='cobra')
old_obj = model.get_objective().keys()
obj = {key: 0 for key in old_obj if key != BIOMASS_ID}
obj[BIOMASS_ID] = 1
model.set_objective(obj)
envcond = OrderedDict()
envcond.update({GLC: (-10.0, 999999.0), O2: (-12.25, 100000.0)})
simulation = get_simulator(model, envcond=envcond)
res = simulation.simulate(method=SimulationMethod.pFBA)
reference = res.fluxes
return {
'model': model,
'biomass': BIOMASS_ID,
'envcond': envcond,
'reference': reference,
'non_target': []
}
def load_gecko():
"""Loads yeast GECKO model using REFRAMED
"""
from mewpy.model.gecko import GeckoModel
model = GeckoModel('single-pool')
simul = get_simulator(model)
simul.summary()
def setUp(self):
"""Set up
Loads a model
"""
from reframed.io.sbml import load_cbmodel
model = load_cbmodel(EC_CORE_MODEL)
from mewpy.simulation import get_simulator
self.simul = get_simulator(model)
self.BIOMASS_ID = model.biomass_reaction
self.SUCC = 'R_EX_succ_e'
def setUp(self):
"""Set up
Loads a model
"""
from cobra.io.sbml import read_sbml_model
model = read_sbml_model(EC_CORE_MODEL)
from mewpy.simulation import get_simulator
self.simul = get_simulator(model)
k = list(self.simul.objective.keys())
self.BIOMASS_ID = k[0]
self.SUCC = 'EX_succ_e'
def load_cobra():
"""Load a model using COBRApy
"""
DIR = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(DIR, '../models/optram/')
DATA_FILE = os.path.join(PATH, "yeast_7.6-optram.xml")
from cobra.io import read_sbml_model
model = read_sbml_model(DATA_FILE)
simul = get_simulator(model)
simul.summary()
def test1(compound='R_EX_tyr__L_e'):
"""
AutoPACMEN, "Automatic construction of metabolic models with enzyme constraints"
(https://doi.org/10.1186/s12859-019-3329-9), is able to construct GECKO like models.
This example optimizes the production of a compound using the E. coli autoPACMEN model
where enzymes are defined as pseudo reactants.
"""
DIR = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(DIR, '../models/autopacmen/')
DATA_FILE = os.path.join(PATH, "iJO1366_2019_06_25_GECKO.xml")
model = SMomentModel(DATA_FILE, enzyme_reaction_prefix='R__TG_ER_')
print(model.proteins)
sim = get_simulator(model)
sim.set_objective("R_BIOMASS_Ec_iJO1366_core_53p95M")
solution = sim.simulate()
print(solution)
print('Wild type tyrosine production :', solution.fluxes['R_EX_tyr__L_e'])
from mewpy.optimization.evaluation import BPCY, WYIELD
from mewpy.simulation import SimulationMethod
from collections import OrderedDict
envcond = OrderedDict()
envcond.update({'R_EX_glc__D_e': (-10.0, 100000.0)})
# the evaluation (objective) functions
evaluator_1 = BPCY("R_BIOMASS_Ec_iJO1366_core_53p95M",
compound,
method=SimulationMethod.lMOMA)
evaluator_2 = WYIELD("R_BIOMASS_Ec_iJO1366_core_53p95M", compound)
problem = GeckoOUProblem(model,
fevaluation=[evaluator_1, evaluator_2],
envcond=envcond,
candidate_max_size=6,
prot_prefix='R__TG_ER_')
# A new instance of the EA optimizer
from mewpy.optimization import EA
ea = EA(problem, max_generations=500)
# runs the optimization
final_pop = ea.run()
from mewpy.util.io import population_to_csv
from time import time
millis = int(round(time() * 1000))
filename = "sMOMEMT{}_OU_{}.csv".format(compound, millis)
population_to_csv(problem, final_pop, filename, simplify=False)
def load_reframed():
"""
Loads a model with REFRAMED
"""
DIR = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(DIR, '../models/optram/')
DATA_FILE = os.path.join(PATH, "yeast_7.6-optram.xml")
from reframed.io.sbml import load_cbmodel
model = load_cbmodel(DATA_FILE, flavor='cobra')
model.summary()
simul = get_simulator(model)
simul.summary()
def load_ec2():
"""Loads a configuration for optimizations on E.coli model iML1515,
including the model, environmental conditions, non targets for modifications,
the biomass equation ID, and wild type reference flux values.
Returns: A dictionary constaining the configuration.
"""
DIR = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(DIR, '../models/ec/')
DATA_FILE = os.path.join(PATH, "iML1515.xml")
NON_TARGET_FILE = os.path.join(
PATH, "nontargets#RK#iJO1366SL#[lim-aerobic#glucose].txt")
BIOMASS_ID = 'R_BIOMASS_Ec_iML1515_core_75p37M'
O2 = 'R_EX_o2_e'
GLC = 'R_EX_glc__D_e'
model = load_cbmodel(DATA_FILE, flavor='cobra')
old_obj = model.get_objective().keys()
obj = {key: 0 for key in old_obj if key != BIOMASS_ID}
obj[BIOMASS_ID] = 1
model.set_objective(obj)
non_target = [O2, GLC, 'R_ATPM']
with open(NON_TARGET_FILE) as f:
line = f.readline()
while line:
non_target.append(line.strip())
line = f.readline()
envcond = OrderedDict()
envcond.update({GLC: (-10.0, 100000.0), O2: (-9.66, 100000.0)})
simulation = get_simulator(model, envcond=envcond)
res = simulation.simulate(method=SimulationMethod.pFBA)
reference = res.fluxes
res = simulation.simulate()
print(res)
return {
'model': model,
'biomass': BIOMASS_ID,
'envcond': envcond,
'reference': reference,
'non_target': non_target
}
def test2(compoud='R_EX_tyr__L_e', filename=None):
"""
AutoPACMEN, "Automatic construction of metabolic models with enzyme constraints"
(https://doi.org/10.1186/s12859-019-3329-9), is able to construct GECKO like models.
This example optimizes the production of a compound using the E. coli autoPACMEN model
where enzymes are defined as pseudo reactants.
The model defines a linear constraint over the protein pool as reactant, by adding the
protein pool as a metabolite in the stochiometric matrix.
Therefore, the model may be treated as a regular GSM.
"""
DIR = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(DIR, '../models/autopacmen/')
DATA_FILE = os.path.join(PATH, "iJO1366_sMOMENT_2019_06_25.xml")
from reframed.io.sbml import load_cbmodel
model = load_cbmodel(DATA_FILE)
sim = get_simulator(model)
s = sim.simulate()
print(s)
# print(s.fluxes)
print('Wildtype tyrosine production :', s.fluxes['R_EX_tyr__L_e'])
print('Pool:', s.fluxes['R_ER_pool_TG_'])
fva = sim.FVA(reactions=['R_ER_pool_TG_'])
print(fva)
# implements a knockout over genes that encode enzymes
BIOMASS_ID = 'R_BIOMASS_Ec_iJO1366_WT_53p95M'
PRODUCT_ID = compoud
from mewpy.optimization.evaluation import BPCY, WYIELD
from mewpy.simulation import SimulationMethod
evaluator_1 = BPCY(BIOMASS_ID, PRODUCT_ID, method=SimulationMethod.lMOMA)
evaluator_2 = WYIELD(BIOMASS_ID, PRODUCT_ID)
# evaluator_3 = TargetFlux(PRODUCT_ID)
from mewpy.problems.genes import GOUProblem
problem = GOUProblem(model,
fevaluation=[evaluator_1, evaluator_2],
max_candidate_size=30)
print(problem.target_list)
from mewpy.optimization import EA
ea = EA(problem, max_generations=20, mp=True)
final_pop = ea.run()
if filename:
print("Simplifying and saving solutions to file")
population_to_csv(problem, final_pop, filename, simplify=False)
def load_ec_gecko():
""" Loads a GECKO like model from AUTOPACMEN
"""
DIR = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(DIR, '../models/autopacmen/')
DATA_FILE = os.path.join(PATH, "iJO1366_2019_06_25_GECKO.xml")
from mewpy.model.gecko import GeckoModel
from reframed.io.sbml import load_cbmodel
cbmodel = load_cbmodel(DATA_FILE)
# ='R_ER_pool_TG_'
model = GeckoModel(cbmodel,
biomass_reaction_id='R_BIOMASS_Ec_iJO1366_WT_53p95M',
protein_reaction_id='R_PROTRS_TG_1',
common_protein_pool_id='M_prot_pool')
simul = get_simulator(model)
for rxn in simul.reactions:
if "R_PROT" in rxn:
print(rxn)
def setUp(self):
import cobra.test
model = cobra.test.create_test_model("textbook")
_BIOMASS_ID = 'Biomass_Ecoli_core'
envcond = {'EX_glc__D_e': (-10, 100000.0)}
from mewpy.simulation import get_simulator
sim = get_simulator(model, envcond=envcond)
sim.objective = _BIOMASS_ID
from mewpy.regulation import RFBAModel
rfba = RFBAModel.from_tabular_format(OPTORF_REG, model, sim,
sep=',', id_col=1, rule_col=2, aliases_cols=[0], header=0)
rfba.update_aliases_from_tabular_format_file(OPTORF_ALIASES, id_col=1, aliases_cols=[0])
initial_state = {var: 1 for var in rfba.targets}
initial_state.update({_BIOMASS_ID: 0.1})
rfba.initial_state = initial_state
_PRODUCT_ID = "EX_succ_e"
from mewpy.optimization.evaluation import BPCY, WYIELD
evaluator_1 = BPCY(_BIOMASS_ID, _PRODUCT_ID, method='pFBA')
evaluator_2 = WYIELD(_BIOMASS_ID, _PRODUCT_ID)
from mewpy.regulation.optorf import OptOrfProblem
self.problem = OptOrfProblem(model, [evaluator_1, evaluator_2], rfba, candidate_max_size=6)
def gecko_ec():
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(dir_path, '../models/gecko/')
DATA_FILE = os.path.join(PATH, 'eciML1515_batch.xml')
from reframed.io.sbml import load_cbmodel
m = load_cbmodel(DATA_FILE)
model = GeckoModel(m,
biomass_reaction_id='R_BIOMASS_Ec_iML1515_core_75p37M',
protein_pool_exchange_id='R_prot_pool_exchange',
reaction_prefix='R_')
model.set_objective({'R_BIOMASS_Ec_iML1515_core_75p37M': 1.0})
# change protein pool bound (suggested by Leslie)
# model.reactions['R_prot_pool_exchange'].ub = 0.26
from mewpy.simulation import get_simulator, SimulationMethod
c = {
'P32131': 0.125,
'P45425': 32,
'P0A6E1': 32,
'P0A9I8': 0,
'P52643': 4,
'P37661': 0.125
}
from mewpy.optimization.evaluation import BPCY, WYIELD
from mewpy.problems import GeckoOUProblem
# the evaluation (objective) functions
evaluator_1 = BPCY("R_BIOMASS_Ec_iML1515_core_75p37M",
'R_EX_tyr__L_e',
method=SimulationMethod.pFBA)
# FVA MAX is strangely very high... changing the default alpha (0.3) to compensate..
evaluator_2 = WYIELD("R_BIOMASS_Ec_iML1515_core_75p37M",
'R_EX_tyr__L_e',
alpha=0.01)
# The optimization problem
problem = GeckoOUProblem(model,
fevaluation=[evaluator_1, evaluator_2],
envcond={},
prot_prefix='R_draw_prot_',
candidate_max_size=30)
# c1 = problem.translate(c,reverse=True)
# print(c1)
# c2 = problem.decode(c1)
# print(c2)
sim = get_simulator(model)
# c2 = {'R_draw_prot_P0AFV4': 0.0, 'R_draw_prot_P67910': (0.0, 0.0), 'R_draw_prot_P02924': (0.0, 0.0),
# 'R_draw_prot_P07639': (1.1271272290940493e-07, 10000), 'R_draw_prot_P39172': 0.0,
# 'R_draw_prot_P0AER3': (0.0, 10000), 'R_draw_prot_P0A991': (0.0, 10000), 'R_draw_prot_P0ACD8': (0.0, 0.0),
# 'R_draw_prot_P00805': (0.0, 10000), 'R_draw_prot_P28635': (0.0, 0.0), 'R_draw_prot_P33593': (0.0, 0.0),
# 'R_draw_prot_P0A9H5': (0.0, 10000), 'R_draw_prot_P0A6L4': (0.0, 10000), 'R_draw_prot_P60560': (0.0, 10000),
# 'R_draw_prot_P37001': 0.0, 'R_draw_prot_P37355': (0.0, 0.0), 'R_draw_prot_P0AEE5': (0.0, 0.0),
# 'R_draw_prot_P0ABA0': (0.0, 0.0), 'R_draw_prot_P0ABK5': (0.0, 10000)}
c2 = {
'R_draw_prot_P69922': (0.0, 10000),
'R_draw_prot_P32176': (0.0, 0.0),
'R_draw_prot_P11349': (0.0, 10000),
'R_draw_prot_P37646': 0.0,
'R_draw_prot_P76577': (0.0, 0.0),
'R_draw_prot_P77788': (0.0, 0.0),
'R_draw_prot_P0AG20': (0.0, 10000),
'R_draw_prot_P63224': 0.0,
'R_draw_prot_P62623': (0.0, 3.28753677758505e-07),
'R_draw_prot_P10907': (0.0, 0.0),
'R_draw_prot_P0AER5': (0.0, 0.0),
'R_draw_prot_P27254': (0.0, 10000),
'R_draw_prot_P0A6E1': (4.0254906190734055e-05, 10000),
'R_draw_prot_P0A924': 0.0,
'R_draw_prot_P32055': (0.0, 10000),
'R_draw_prot_P21179': 0.0,
'R_draw_prot_P10378': 0.0
}
print("\nFBA")
res = sim.simulate(method=SimulationMethod.FBA, constraints=c2)
print(res)
print("Biomass:", res.fluxes['R_BIOMASS_Ec_iML1515_core_75p37M'])
print("TYR:", res.fluxes['R_EX_tyr__L_e'])
print("\npFBA")
res = sim.simulate(method=SimulationMethod.pFBA, constraints=c2)
print(res)
print("Biomass:", res.fluxes['R_BIOMASS_Ec_iML1515_core_75p37M'])
print("TYR:", res.fluxes['R_EX_tyr__L_e'])
print("\nlMOMA")
res = sim.simulate(method=SimulationMethod.lMOMA, constraints=c2)
print(res)
print("Biomass:", res.fluxes['R_BIOMASS_Ec_iML1515_core_75p37M'])
print("TYR:", res.fluxes['R_EX_tyr__L_e'])
print("\nMOMA")
res = sim.simulate(method=SimulationMethod.MOMA, constraints=c2)
print(res)
print("Biomass:", res.fluxes['R_BIOMASS_Ec_iML1515_core_75p37M'])
print("TYR:", res.fluxes['R_EX_tyr__L_e'])
print("\nFVA")
res = sim.FVA(reactions=['R_EX_tyr__L_e'], constraints=c2)
print(res)
def setUp(self):
from mewpy.model.gecko import GeckoModel
model = GeckoModel('single-pool')
from mewpy.simulation import get_simulator
self.simul = get_simulator(model)
def test_simulator(self):
from mewpy.model.gecko import GeckoModel
model = GeckoModel('single-pool')
from mewpy.simulation import get_simulator
get_simulator(model)