def run_detfl(yaml_file, uptake_funs, medium_funs=dict(), model=None, ini_sol=None): # pass in
params = read_config(yaml_file)
for key, value in params.items():
print('%s: %s' % (key, value))
if model is None:
model = load_json_model(params['model'])
standard_solver_config(model)
model.solver.configuration.verbosity = params['options']['verbose']
time_data = run_dynamic_etfl(model,
timestep=params['simulation']['timestep'],
tfinal=params['simulation']['tfinal'],
uptake_fun=uptake_funs,
medium_fun=medium_funs,
uptake_enz=params['assumptions']['uptake_enz'],
S0=params['assumptions']['S0'],
X0=params['assumptions']['X0'],
inplace=params['options']['inplace'],
initial_solution = ini_sol,
chebyshev_include = params['options']['chebyshev_include'],
dynamic_constraints = params['options']['constraints']
)
# plot_dynamics(model, time_data)
out_path = join('outputs',params['tag']+get_timestr())
makedirs(out_path)
time_data.to_csv(join(out_path,'solution.csv'))
write_yaml(params,join(out_path,'config.yaml'))
return time_data
from cobra.flux_analysis import single_gene_deletion
from tqdm import tqdm
import time
solver = 'optlang-gurobi'
#
# ecoli_fba = cobra.io.json.load_json_model('models/iJO1366_T0E0N0__20180606_121758.json')
# ecoli_fba.solver = solver
# ecoli_tfa = pytfa.io.json.load_json_model('models/iJO1366_T1E0N0__20180606_121751.json')
# ecoli_tfa.solver = solver
# vETFL
ecoli = load_json_model(
'models/SlackModel iJO1366_vETFL_431_enz_128_bins__20190701_082518.json')
# vETFL_infer:
#ecoli = load_json_model('models/SlackModel
# iJO1366_vETFL_infer_2084_enz_128_bins__20190624_095428.json')
ecoli.solver = solver
standard_solver_config(ecoli)
ecoli.optimize()
max_growth = ecoli.solution.objective_value
print('Growth : {}'.format(ecoli.solution.objective_value))
print(' - Ribosomes produced: {}'.format(ecoli.solution.raw.EZ_rib))
print(' - RNAP produced: {}'.format(ecoli.solution.raw.EZ_rnap))
# --
vec_dict = {'plasmid_pET-AR-ALS': get_bdo_plasmid, 'debug': get_debug_plasmid}
def write_conf(model, conf):
filename = 'outputs/' + conf['tag'] + '/config.yaml'
conf['objective'] = str(model.objective.expression)
with open(filename, 'w') as fid:
yaml.dump(config, fid)
if __name__ == '__main__':
config = read_config(CONFIG)
if config['model'] != 'debug':
model = load_json_model(config['model'],
solver=config['options']['solver'])
else:
from etfl.tests.small_model import create_etfl_model
model = create_etfl_model(0, 1, solver=config['options']['solver'])
standard_solver_config(model)
model.solver.configuration.verbosity = config['options']['verbose']
copy_number = int(config['simulation']['copy_number'])
vector_generator = vec_dict[config['simulation']['vector']]
has_rnap = config['simulation']['add_rnap']
my_plasmid = vector_generator(model, has_rnap)
#####################
# Model integration #
medium_funs = {
'EX_glc__D_e': glc_fun,
'EX_lcts_e': lac_fun,
'EX_o2_e': o2_diff,
'EX_ac_e': ac_fun,
}
uptake_funs = get_uptake_funs()
if not has_lcts:
medium_funs.pop('EX_lcts_e')
uptake_funs.pop('EX_lcts_e')
if config['model'] != 'debug':
model = load_json_model(config['model'])
else:
from etfl.tests.small_model import create_etfl_model
model = create_etfl_model(0, 0)
standard_solver_config(model)
model.solver.configuration.verbosity = 0
ini_sol = prepare_model(model,
v0=config['assumptions']['v0'],
S0=config['assumptions']['S0'],
uptake_fun=uptake_funs)
time_data = run_detfl(model=model,
yaml_file=CONFIG,
ini_sol=ini_sol,
import pandas as pd
#
# model = create_etfl_model( 0,0,
# n_mu_bins = 128,
# )
# # model.reactions.ATPM.lower_bound = 4
# model.reactions.EX_glc__D_e.lower_bound = -1
# model.optimize()
# model.objective = model.growth_reaction.forward_variable + \
# model.reactions.EX_glc__D_e.reverse_variable
from etfl.io.json import load_json_model
model = load_json_model('models/iJO1366_T0E1N1_431_enz_128_bins__20180926_135704.json')
# model = load_json_model('models/RelaxedModel iJO1366_T1E1N1_431_enz_128_bins__20180926_124941.json')
def get_enzymes_of_subsystem(model, subsystem):
reactions = [x for x in model.reactions if subsystem.lower() in x.subsystem.lower()]
enzymes = [item for rxn in reactions if hasattr(rxn,'enzymes')
for item in rxn.enzymes
]
return enzymes
def get_mrna_total(time_data, mrnas):
return time_data.loc[[x.variable.name * x.scaling_factor
for x in mrnas]].sum()
from math import exp
import pandas as pd
#
# model = create_etfl_model( 0,0,
# n_mu_bins = 128,
# )
# # model.reactions.ATPM.lower_bound = 4
# model.reactions.EX_glc__D_e.lower_bound = -1
# model.optimize()
# model.objective = model.growth_reaction.forward_variable + \
# model.reactions.EX_glc__D_e.reverse_variable
from etfl.io.json import load_json_model
# model = load_json_model('models/SlackModel iJO1366_vETFL_2084_enz_128_bins__20190122_170118.json')
model = load_json_model(
'models/iJO1366_vEFL_431_enz_128_bins__20190121_090316.json')
# model = load_json_model('models/SlackModel iJO1366_vETFL_431_enz_128_bins__20190122_145700.json')
# model = load_json_model('models/iJO1366_T0E1N1_431_enz_128_bins__20180926_135704.json')
# model = load_json_model('models/RelaxedModel iJO1366_T1E1N1_431_enz_128_bins__20180926_124941.json')
model_tag = 'vEFL_mixed_lcts2_with_degradation'
# for rxn in model.translation_reactions:
# rxn.subsystem = 'Translation'
# for rxn in model.transcription_reactions:
# rxn.subsystem = 'Transcription'
exchanges = ['EX_glc__D_e', 'EX_ac_e', 'EX_o2_e', 'EX_lcts_e']
concentrations = ['X'] + ['S_' + x for x in exchanges]
from tqdm import tqdm
import time
solver = 'optlang-gurobi'
#
# ecoli_fba = cobra.io.json.load_json_model('models/iJO1366_T0E0N0__20180606_121758.json')
# ecoli_fba.solver = solver
# ecoli_tfa = pytfa.io.json.load_json_model('models/iJO1366_T1E0N0__20180606_121751.json')
# ecoli_tfa.solver = solver
# ecoli = load_json_model('models/RelaxedModel iJO1366_vETFL_431_enz_128_bins__20190108_181346.json')
# ecoli = load_json_model('models/SlackModel iJO1366_vETFL_431_enz_128_bins__20190122_155755.json')
# ecoli = load_json_model('models/SlackModel iJO1366_vETFL_1783_enz_128_bins__20190221_185154.json')
ecoli = load_json_model(
'models/SlackModel iJO1366_vETFL_2084_enz_128_bins__20190122_170118.json')
ecoli.solver = solver
standard_solver_config(ecoli)
ecoli.optimize()
max_growth = ecoli.solution.objective_value
print('Growth : {}'.format(ecoli.solution.objective_value))
print(' - Ribosomes produced: {}'.format(ecoli.solution.raw.EZ_rib))
print(' - RNAP produced: {}'.format(ecoli.solution.raw.EZ_rnap))
# --
def ko_gene(model, gene_id):
try:
import numpy as np
import bokeh.plotting as bp
from bokeh.models import Whisker, ColumnDataSource
from etfl.io.json import load_json_model
import cobra
from bokeh.io import export_svgs
bp.curdoc().clear()
ec_cobra = cobra.io.load_json_model('iJO1366_with_xrefs.json')
# ec_cobra.reactions.ATPM.lower_bound = 0
growth_reaction_id = 'BIOMASS_Ec_iJO1366_WT_53p95M'
ecoli = load_json_model(
'models/RelaxedModel iJO1366_T1E1N1_350_enz_256_bins__20180830_121200.json'
)
uptake_range = pd.Series(list(range(100)))
def simulate(uptake, model):
model.reactions.EX_glc__D_e.lower_bound = -1 * uptake
model.reactions.EX_glc__D_e.upper_bound = 0
model.objective = growth_reaction_id
model.objective.direction = 'max'
try:
sol = model.optimize()
except Exception:
return pd.Series([np.nan, np.nan])
return pd.Series([sol.f, model.reactions.EX_glc__D_e.flux * -1])
from math import exp
import pandas as pd
#
# model = create_etfl_model( 0,0,
# n_mu_bins = 128,
# )
# # model.reactions.ATPM.lower_bound = 4
# model.reactions.EX_glc__D_e.lower_bound = -1
# model.optimize()
# model.objective = model.growth_reaction.forward_variable + \
# model.reactions.EX_glc__D_e.reverse_variable
from etfl.io.json import load_json_model
model = load_json_model('models/SlackModel iJO1366_vETFL_440_enz_128_bins__20190227_141232.json')
# model = load_json_model('models/SlackModel iJO1366_vETFL_2084_enz_128_bins__20190122_170118.json')
# model = load_json_model('models/iJO1366_vEFL_431_enz_128_bins__20190121_090316.json')
# model = load_json_model('models/SlackModel iJO1366_vETFL_431_enz_128_bins__20190122_145700.json')
# model = load_json_model('models/iJO1366_T0E1N1_431_enz_128_bins__20180926_135704.json')
# model = load_json_model('models/RelaxedModel iJO1366_T1E1N1_431_enz_128_bins__20180926_124941.json')
model_tag = 'dvETFL_deg_glc_lcts_alldelta_noexpc'
# for rxn in model.translation_reactions:
# rxn.subsystem = 'Translation'
# for rxn in model.transcription_reactions:
# rxn.subsystem = 'Transcription'
exchanges = [
import time
solver = 'optlang-gurobi'
USE_GPR = False
#
# ecoli_fba = cobra.io.json.load_json_model('models/iJO1366_T0E0N0__20180606_121758.json')
# ecoli_fba.solver = solver
# ecoli_tfa = pytfa.io.json.load_json_model('models/iJO1366_T1E0N0__20180606_121751.json')
# ecoli_tfa.solver = solver
# vETFL
# ecoli = load_json_model('models/SlackModel iJO1366_vETFL_v_0.12_431_enz_128_bins__20200605_083556.json')
# ecoli = load_json_model('models/SlackModel iJO1366_vETFL_mean_kcat_431_enz_128_bins__20200605_090017.json')
# vETFL_infer:
ecoli = load_json_model('models/SlackModel iJO1366_vETFL_infer_infer_2084_enz_128_bins__20200605_102233.json')
ecoli.solver = solver
standard_solver_config(ecoli)
ecoli.optimize()
max_growth = ecoli.solution.objective_value
print('Growth : {}'.format(ecoli.solution.objective_value))
print(' - Ribosomes produced: {}'.format(ecoli.solution.raw.EZ_rib))
print(' - RNAP produced: {}'.format(ecoli.solution.raw.EZ_rnap))
# --
def safe_optim(model):
etfl_classif = y_true.copy()
etfl_classif[etfl_tp.index] = 'tp'
etfl_classif[etfl_tn.index] = 'tn'
etfl_classif[etfl_fp.index] = 'fp'
etfl_classif[etfl_fn.index] = 'fn'
ijo_classif = y_true.copy()
ijo_classif[ijo_tp['genes']] = 'tp'
ijo_classif[ijo_tn['genes']] = 'tn'
ijo_classif[ijo_fp['genes']] = 'fp'
ijo_classif[ijo_fn['genes']] = 'fn'
from etfl.io.json import load_json_model
from etfl.core.reactions import EnzymaticReaction
m = load_json_model('models/SlackModel '
'iJO1366_vETFL_431_enz_128_bins__20190701_082518.json',
solver='cplex')
def enzrxn_to_gpr(rxn):
return ' OR '.join([' AND '.join(['{}*{}'.format(v,k)
for v,k in isozyme.composition.items()])
for isozyme in rxn.enzymes])
def enzrxn_to_iso(rxn):
return ' OR '.join([isozyme.id for isozyme in rxn.enzymes])
list_of_offenders = list()
for gene_id in etfl_classif.index:
if etfl_classif[gene_id] != ijo_classif[gene_id]:
rxns = [x for x in m.reactions if any(gene_id == g.id for g in x.genes)]
for rxn in rxns:
ijo_GPR = rxn.gene_reaction_rule
from etfl.io.json import load_json_model
from etfl.optim.variables import mRNAVariable, EnzymeVariable
from etfl.optim.utils import fix_integers
from pytfa.analysis import variability_analysis, \
apply_reaction_variability, \
apply_generic_variability, \
apply_directionality
from etfl.analysis.utils import enzymes_to_peptides_conc
import pandas as pd
ecoli = load_json_model('models/RelaxedModel iJO1366_T1E1N1_431_enz_128_bins__20180926_124941.json')
ecoli.optimize()
def print_sol(model):
print('Objective : {}'.format(model.solution.f))
print(' - Glucose uptake : {}'.format(model.solution.x_dict.EX_glc__D_e))
print(' - Growth : {}'.format(model.solution.x_dict[model.growth_reaction.id]))
print(' - Ribosomes produced: {}'.format(model.solution.raw.EZ_rib))
print(' - RNAP produced: {}'.format(model.solution.raw.EZ_rnap))
try:
print(' - DNA produced: {}'.format(model.solution.raw.DN_DNA))
except AttributeError:
pass
# ID 109686
# Property Glucose uptake rate of strain C-3000 in minimal M9 media
from etfl.io.json import load_json_model
from etfl.optim.variables import mRNAVariable, EnzymeVariable
from etfl.optim.utils import fix_integers
from pytfa.analysis import variability_analysis, \
apply_reaction_variability, \
apply_generic_variability, \
apply_directionality
from etfl.analysis.utils import enzymes_to_peptides_conc
import pandas as pd
ecoli = load_json_model(
'models/SlackModel iJO1366_vETFL_431_enz_128_bins__20190122_145700.json')
ecoli.optimize()
def print_sol(model):
print('Objective : {}'.format(model.solution.objective_value))
print(' - Glucose uptake : {}'.format(
model.solution.raw.loc['EX_glc__D_e']))
print(' - Growth : {}'.format(
model.solution.raw.loc[model.growth_reaction.id]))
print(' - Ribosomes produced: {}'.format(model.solution.raw.loc['EZ_rib']))
print(' - RNAP produced: {}'.format(model.solution.raw.loc['EZ_rnap']))
try:
print(' - DNA produced: {}'.format(model.solution.raw.DN_DNA))
except AttributeError:
pass
model_files = {
# 'EFL':'iJO1366_EFL_1783_enz_128_bins__20190221_165945.json',
# 'ETFL':'SlackModel iJO1366_ETFL_1783_enz_128_bins__20190221_172509.json',
# 'vEFL':'iJO1366_vEFL_1783_enz_128_bins__20190221_182321.json',
# 'vETFL':'SlackModel iJO1366_vETFL_1783_enz_128_bins__20190221_185154.json',
# 'vETFL65':'SlackModel iJO1366_vETFL_1783_enz_128_bins__20190221_194424.json',
# 'vETFL_infer':'SlackModel iJO1366_vETFL_infer_2088_enz_128_bins__20190221_204202.json',
# 'vETFL65_infer': 'SlackModel iJO1366_vETFL_infer_2088_enz_128_bins__20190221_220847.json',
#
'EFL':'iJO1366_EFL_431_enz_128_bins__20190121_080047.json',
'ETFL':'SlackModel iJO1366_ETFL_431_enz_128_bins__20190121_110027.json',
'vEFL':'iJO1366_vEFL_431_enz_128_bins__20190121_090316.json',
'vETFL':'SlackModel iJO1366_vETFL_431_enz_128_bins__20190122_145700.json',
'vETFL65':'SlackModel iJO1366_vETFL_431_enz_128_bins__20190122_155755.json',
'vETFL_infer':'SlackModel iJO1366_vETFL_2084_enz_128_bins__20190122_170118.json',
'vETFL65_infer':'SlackModel iJO1366_vETFL_2084_enz_128_bins__20190124_082824.json',
}
models = {k:load_json_model('models/'+v,solver=solver) for k,v in model_files.items()}
data = {}
for name,model in models.items():
growth_uptake_config(model)
model.warm_start = None
model.logger.info('Simulating ...')
start = time()
data[name] = uptake_range.apply(simulate, args=[model,variables])
stop = time()
print('Elapsed time: {}'.format(stop - start))
data[name].to_csv('outputs/benchmark_{}.csv'.format(name))
from collections import OrderedDict
from bokeh.palettes import Category10
from bokeh.io import export_svgs
from bokeh.layouts import row
from os.path import join as pjoin
bp.curdoc().clear()
data_dir = '../organism_data/info_ecoli'
from etfl.io.json import load_json_model
# scaling_model = load_json_model('models/RelaxedModel iJO1366_T1E1N1_431_enz_128_bins__20180926_124941.json')
scaling_model = load_json_model(
'models/iJO1366_EFL_431_enz_128_bins__20190121_080047.json')
def make_polygon(x, ymin, ymax):
lx = x.tolist()
lymin = ymin.tolist()
lymax = ymax.tolist()
return lx + lx[::-1], lymin + lymax[::-1]
def make_bars(x, ymin, ymax, width=None):
data = pd.DataFrame([x, ymin, ymax]).T
data.columns = ['x', 'bottom', 'top']
data.dropna(how='any', inplace=True)
'iJO1366_EFL_431_enz_128_bins__20190620_080807.json',
'ETFL':
'SlackModel iJO1366_ETFL_431_enz_128_bins__20190701_122635.json',
'vEFL':
'iJO1366_vEFL_431_enz_128_bins__20190620_084052.json',
'vETFL':
'SlackModel iJO1366_vETFL_431_enz_128_bins__20190701_082518.json',
'vETFLmean':
'SlackModel iJO1366_vETFL_mean_kcat_431_enz_128_bins__20190701_070448.json',
'vETFL_infer':
'SlackModel iJO1366_vETFL_infer_2084_enz_128_bins__20190624_095428.json',
'vETFLmean_infer':
'SlackModel iJO1366_vETFL_infer_mean_kcat_infer_2084_enz_128_bins__20190701_070427.json',
}
models = {
k: load_json_model('models/' + v, solver=solver)
for k, v in model_files.items()
}
data = {}
for name, model in models.items():
growth_uptake_config(model)
model.warm_start = None
model.logger.info('Simulating ...')
start = time()
data[name] = uptake_range.apply(simulate, args=[model, variables])
stop = time()
print('Elapsed time: {}'.format(stop - start))
data[name].to_csv('outputs/benchmark_{}.csv'.format(name))
def test_read():
m = load_json_model(savepath)
m.optimize()
