def test_basic_gecko_adjustment():
"""Tests basic adjustments
"""
in_model = {
'P00549': 0.1,
'P31373': 0.1,
'P31382': 0.1,
'P39708': 0.1,
'P39714': 0.1,
'P39726': 0.1,
'Q01574': 0.1
}
not_in_model = {'P10591': 0.1, 'P31383': 0.1, 'P32471': 0.1}
measurements = pd.concat([pd.Series(in_model), pd.Series(not_in_model)])
model = GeckoModel('multi-pool')
model.limit_proteins(fractions=pd.Series(measurements))
simul = GeckoSimulation(model)
sol = simul.simulate()
assert sol.objective_value > 0.05
assert len(model.proteins) - len(model.pool_proteins) - len(in_model) == 0
assert all(model.reactions[rxn].ub > 0
for rxn in model.individual_protein_exchanges)
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 yeast_gecko_ou(compound, display=False, filename=None):
""" GECKO enzyme deletion example.
It runs a multi objective optimization for the increased production of a certain compound on E. Coli.
The GECKO model is the yeast model companion from the GECKO paper "Improving the phenotype predictions
of a yeast genome‐scale metabolic model by incorporating enzymatic
constraints" https://doi.org/10.15252/msb.20167411.
Runs over the MEWpy implementation.
:param compound: A target reaction identifier.
:param display: Prints the best solution.
:param filename: If given, saves the results as csv to filename.
"""
model = GeckoModel('single-pool')
BIOMASS = 'r_2111'
envcond = envcond = {'r_1714_REV': (-10, 100000)}
# the evaluation (objective) functions
evaluator_1 = BPCY(BIOMASS, compound, method='lMOMA')
evaluator_2 = WYIELD(BIOMASS, compound)
# The optimization problem
problem = GeckoOUProblem(model,
fevaluation=[evaluator_1, evaluator_2],
envcond=envcond,
candidate_max_size=6)
# A new instance of the EA optimizer
ea = EA(problem, max_generations=ITERATIONS)
# runs the optimization
final_pop = ea.run()
# optimization results
if display:
individual = max(final_pop)
best = list(problem.decode(individual.candidate).keys())
print('Best Solution: \n{0}'.format(str(best)))
# save final population to file
if filename:
print("Simplifying and saving solutions to file")
population_to_csv(problem, final_pop, filename, simplify=False)
))
return jobs
if __name__ == '__main__':
# Run the study
output_directory = 'data'
# Configure the experiments
from mewpy.model.gecko import GeckoModel
from collections import OrderedDict
compound = 'r_1913'
model = GeckoModel('single-pool', biomass_reaction_id='r_2111')
model.set_objective({'r_2111': 1.0, 'r_4041': 0.0})
envcond = OrderedDict()
from mewpy.optimization.evaluation import BPCY, WYIELD
from mewpy.simulation import SimulationMethod
# the evaluation (objective) functions
evaluator_1 = BPCY("r_2111", compound, method=SimulationMethod.lMOMA)
evaluator_2 = WYIELD("r_2111", compound)
from mewpy.problems import GeckoOUProblem
p = GeckoOUProblem(model,
fevaluation=[evaluator_1, evaluator_2],
candidate_max_size=candidate_max_size)
def ec_gecko_ou(compound, display=False, filename=None):
""" GECKO enzyme over/under expression example.
It runs a multi objective optimization for the increased production of a certain compound on E. Coli.
The GECKO model is the yeast model companion from the GECKO paper "Improving the phenotype predictions
of a yeast genome‐scale metabolic model by incorporating enzymatic
constraints" https://doi.org/10.15252/msb.20167411.
Runs over the MEWpy implementation.
:param compound: A target reaction identifier.
:param display: Prints the best solution.
:param filename: If given, saves the results as csv to filename.
"""
# define the default solver
# from reframed.solvers import set_default_solver
# set_default_solver('gurobi')
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(dir_path, '../../../examples/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
# define environmental consitions
envcond = OrderedDict()
# the evaluation (objective) functions
evaluator_1 = BPCY("R_BIOMASS_Ec_iML1515_core_75p37M",
compound,
method=SimulationMethod.MOMA)
# FVA MAX is strangely very high... changing the default alpha (0.3) to compensate..
evaluator_2 = WYIELD("R_BIOMASS_Ec_iML1515_core_75p37M",
compound,
alpha=0.01)
evaluator_3 = TargetFlux(compound)
# The optimization problem
problem = GeckoOUProblem(
model,
fevaluation=[evaluator_1, evaluator_2, evaluator_3],
envcond=envcond,
prot_prefix='R_draw_prot_',
candidate_max_size=30)
# A new instance of the EA optimizer
ea = EA(problem, max_generations=ITERATIONS, mp=True)
# runs the optimization
final_pop = ea.run()
# optimization results
if display:
individual = max(final_pop)
best = list(problem.decode(individual.candidate).keys())
print('Best Solution: \n{0}'.format(str(best)))
# save final population to file
if filename:
print("Simplifying and saving solutions to file")
population_to_csv(problem, final_pop, filename, simplify=False)
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)
def test_gecko(self):
from mewpy.model.gecko import GeckoModel
GeckoModel('single-pool')