def ec_gecko_ko(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.
"""
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})
envcond = OrderedDict()
# the evaluation (objective) functions
evaluator_1 = BPCY("R_BIOMASS_Ec_iML1515_core_75p37M",
compound,
method=SimulationMethod.lMOMA)
evaluator_2 = WYIELD("R_BIOMASS_Ec_iML1515_core_75p37M", compound)
# The optimization problem
problem = GeckoKOProblem(model,
fevaluation=[evaluator_1, evaluator_2],
envcond=envcond,
prot_prefix='R_draw_prot_',
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)
def cb_ou(product, chassis='ec', display=False, filename=None):
"""Defines and run a gene over/under expression problem.
Args:
product (str): the ID of the compound reaction exchange to be optimized
chassis (str, optional): The chassis, 'ec'(E.coli iJO1366Sl) , 'ec2' (E.coli iML1515) or 'ys' (yeast).
Defaults to 'ec'.
display (bool, optional): [description]. Defaults to False.
filename ([type], optional): [description]. Defaults to None.
"""
if chassis == 'ec2':
conf = load_ec2()
elif chassis == 'ys':
conf = load_yeast()
else:
conf = load_ec()
BIOMASS_ID = conf['biomass']
PRODUCT_ID = product
model = conf['model']
envcond = conf['envcond']
reference = conf['reference']
evaluator_1 = BPCY(BIOMASS_ID, PRODUCT_ID, method=SimulationMethod.lMOMA)
evaluator_2 = WYIELD(BIOMASS_ID, PRODUCT_ID)
# Favors deletion and under expression modifications
evaluator_3 = ModificationType()
from mewpy.problems import GOUProblem
problem = GOUProblem(model,
fevaluation=[evaluator_1, evaluator_2, evaluator_3],
envcond=envcond,
reference=reference,
candidate_min_size=4,
candidate_max_size=6,
operators=("lambda x,y: min(x,y)",
"lambda x,y: max(x,y)"),
product=PRODUCT_ID)
ea = EA(problem,
max_generations=ITERATIONS,
visualizer=False,
algorithm='NSGAIII')
final_pop = ea.run()
if display:
individual = max(final_pop)
best = list(problem.decode(individual.candidate).keys())
print('Best Solution: \n{0}'.format(str(best)))
if filename:
print("Simplifying and saving solutions to file")
population_to_csv(problem, final_pop, filename, simplify=False)
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 test():
""" An example on to use OptRAM optimization problems to find
regulatory modification for the increased production of tryptophan.
"""
dir_path = os.path.dirname(os.path.realpath(__file__))
PATH = os.path.join(dir_path, '../../../examples/models/optram/')
gene_file = os.path.join(PATH, 'mgene.csv')
ft_file = os.path.join(PATH, 'TFnames.csv')
matrix_file = os.path.join(PATH, 'regnet.csv')
model_file = os.path.join(PATH, 'yeast_7.6-optram.xml')
BIOMASS_ID = 'r_2111'
PRODUCT_ID = 'r_1912'
GLC = 'r_1714'
envcond = {GLC: (-10, 0)}
# adds the prefix 'G_' to genes. Only for REFRAMED models
regnet = load_optram(gene_file, ft_file, matrix_file, gene_prefix='G_')
# the general objective is to maximize the target
from reframed.io.sbml import load_cbmodel
model = load_cbmodel(model_file)
model.set_objective({BIOMASS_ID: 1})
evaluator_1 = BPCY(BIOMASS_ID, PRODUCT_ID, method=SimulationMethod.lMOMA)
evaluator_2 = WYIELD(BIOMASS_ID, PRODUCT_ID, parsimonious=True)
# OptRAM problem
problem = OptRamProblem(model, [evaluator_1, evaluator_2],
regnet,
envcond=envcond,
candidate_min_size=10,
candidate_max_size=30)
print('Target List:', problem.target_list)
print("\n\n")
print('Metabolic Genes', problem.simulator.genes)
print("\n\n")
ea = EA(problem, max_generations=3, mp=True)
final_pop = ea.run()
from mewpy.util.io import population_to_csv
millis = int(round(time() * 1000))
filename = "OPTRAM{}_OU_{}.csv".format('TRP', millis)
population_to_csv(problem, final_pop, filename, simplify=False)
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 cb_ko(product, chassis='ec', display=False, filename=None):
"""Defines and run a gene deletion problem.
Args:
product (str): the ID of the compound reaction exchange to be optimized
chassis (str, optional): The chassis, 'ec'(E.coli iJO1366Sl) , 'ec2' (E.coli iML1515) or 'ys' (yeast).
Defaults to 'ec'.
display (bool, optional): [description]. Defaults to False.
filename ([type], optional): [description]. Defaults to None.
"""
if chassis == 'ec':
conf = load_ec()
elif chassis == 'ys':
conf = load_yeast()
else:
raise ValueError
BIOMASS_ID = conf['biomass']
PRODUCT_ID = product
model = conf['model']
non_target = conf['non_target']
envcond = conf['envcond']
reference = conf['reference']
evaluator_1 = BPCY(BIOMASS_ID, PRODUCT_ID, method=SimulationMethod.lMOMA)
evaluator_2 = WYIELD(BIOMASS_ID, PRODUCT_ID)
from mewpy.problems.genes import GKOProblem
problem = GKOProblem(model,
fevaluation=[evaluator_1, evaluator_2],
non_target=non_target,
envcond=envcond,
reference=reference)
ea = EA(problem, max_generations=ITERATIONS, mp=True)
final_pop = ea.run()
if display:
individual = max(final_pop)
best = list(problem.decode(individual.candidate).keys())
print('Best Solution: \n{0}'.format(str(best)))
if filename:
print("Simplifying and saving solutions to file")
population_to_csv(problem, final_pop, filename, simplify=False)
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)