def __init__(self,
model_code='ecoli:iJO1366',
solver='gurobi',
min_biomass=0.55):
super().__init__(model_code, solver, min_biomass)
if self.species == 'ecoli':
# self.model.reactions.EX_glc__D_e.lower_bound = -1 * glc_uptake - glc_uptake_std
# self.model.reactions.EX_glc__D_e.upper_bound = -1 * glc_uptake + glc_uptake_std
thermo_db = load_thermoDB(
join(data_dir, 'thermo/thermo_data.thermodb'))
self.model = ThermoModel(thermo_db, self.model)
self.model.name = self.model_name
self.model.sloppy = True
apply_compartment_data(
self.model,
read_compartment_data(
join(data_dir, 'thermo/compartment_data.json')))
self.apply_annotation_data()
self.model.prepare()
self.model.convert()
# self.model.reactions.get_by_id(self.objective).lower_bound = objective_lb
self.model.repair()
try:
self.model.optimize()
except (AttributeError, SolverError):
self.model, _, _ = relax_dgo(self.model, in_place=True)
# self.model.reactions.get_by_id(self.objective).lower_bound = 0
self.model.print_info()
def create_model(model_name,
thermo_data=None,
lexicon=None,
compartment_data=None) -> ThermoModel:
"""
Creates a tFBA-ready model.
Parameters
----------
model_name : str
The name of a model.
thermo_data : dict, optional
A thermodynamic database. If specified, ``lexicon`` and ``compartment data`` are required.
lexicon : pandas.DataFrame, optional
A dataframe linking metabolite IDs to SEED compound IDs. If specified, ``thermo_data`` and ``compartment_data``
are required.
compartment_data : dict, optional
A dictionary containing information about each compartment of the model. If specified, ``thermo_data`` and
``lexicon`` are required.
Returns
-------
pytfa.ThermoModel
A thermodynamic database.
Raises
------
ValueError
If any (but not all) of ``thermo_data``, ``lexicon``, and ``compartment_data`` is None.
"""
data_is_none = [
data is None for data in [thermo_data, lexicon, compartment_data]
]
if all(data_is_none):
thermo_data, lexicon, compartment_data = load_data(model_name)
elif any(data_is_none):
raise ValueError("Not all required data supplied.")
# due to a bug on pytfa, the logger is created with "None" as name
_silence_pytfa(f"thermomodel_{None}")
# however, if the model ends up being copied the correct name will be used, so this logger should be silenced too
_silence_pytfa(f"thermomodel_{model_name}")
cmodel = load_cbm(model_name)
tmodel = ThermoModel(thermo_data, cmodel)
tmodel.name = model_name
annotate_from_lexicon(tmodel, lexicon)
apply_compartment_data(tmodel, compartment_data)
if tmodel.solver.interface.__name__ == "optlang.gurobi_interface":
tmodel.solver.problem.Params.NumericFocus = 3
tmodel.solver.configuration.tolerances.feasibility = 1e-9
tmodel.solver.configuration.presolve = True
tmodel.prepare()
tmodel.convert(verbose=False)
return tmodel
def convert2thermo(model, name):
# Initialize the model
tmodel = pytfa.ThermoModel(thermo_data, model)
tmodel.name = name
# Annotate the model
annotate_from_lexicon(tmodel, lexicon)
apply_compartment_data(tmodel, compartment_data)
## TFA conversion
tmodel.prepare()
tmodel.convert()
tmodel.solver = solver
return tmodel
def create_etfl_model(
has_thermo,
has_neidhardt,
n_mu_bins=64,
mu_max=3,
optimize=True,
):
#------------------------------------------------------------
# Initialisation
#------------------------------------------------------------
# this hack works because we are using the solver switch to update the var
# names in the solver but really we should not do this
# TODO: clean up model.sanitize_varnames
growth_reaction_id = 'Biomass_Ecoli_core'
vanilla_model = create_fba_model()
vanilla_model.objective = growth_reaction_id
fba_sol = vanilla_model.slim_optimize()
mu_0 = fba_sol
mu_range = [0, mu_max]
n_mu_bins = n_mu_bins
coupling_dict = get_coupling_dict(vanilla_model,
mode='kmax',
atps_name='ATPS4r')
# coupling_dict = get_lloyd_coupling_dict(vanilla_model)
# Initialize the model
name = 'small_model_T{:1}E{:1}N{:1}_{}_enz_{}_bins.json'.format(
has_thermo, True, has_neidhardt, len(coupling_dict), n_mu_bins)
# for k,v in coupling_dict.items():
# for enz in v:
# enz.kcat_fwd = enz.kcat_bwd = 1e9
if has_thermo:
thermo_data, lexicon, compartment_data = get_thermo_data()
ecoli = ThermoMEModel(
thermo_data,
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
else:
ecoli = MEModel(
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
ecoli.name = name
ecoli.logger.setLevel(logging.WARNING)
ecoli.sloppy = True
# Solver settings
ecoli.solver = solver
standard_solver_config(ecoli)
if has_thermo:
# Annotate the cobra_model
annotate_from_lexicon(ecoli, lexicon)
apply_compartment_data(ecoli, compartment_data)
# TFA conversion
ecoli.prepare()
ecoli.convert() #add_displacement = True)
nt_sequences = get_nt_sequences()
mrna_dict = get_mrna_dict(ecoli)
rnap = get_rnap()
rib = get_rib()
all_peptides = set([
x for enzymes in coupling_dict.values() for enz in enzymes
for x in enz.composition
])
prune_to_genes = lambda the_dict:{k:v for k,v in the_dict.items() \
if k in vanilla_model.genes or
k in rib.rrna_composition or
k in rib.composition or
k in rnap.composition or
k in all_peptides}
nt_sequences = prune_to_genes(nt_sequences)
mrna_dict = prune_to_genes(mrna_dict)
# Remove nucleotides and amino acids from biomass reaction as they will be
# taken into account by the expression
remove_from_biomass_equation(
model=ecoli,
nt_dict=rna_nucleotides,
aa_dict=aa_dict,
atp_id=essentials['atp'],
adp_id=essentials['adp'],
pi_id=essentials['pi'],
h2o_id=essentials['h2o'],
h_id=essentials['h'],
)
##########################
## MODEL CREATION ##
##########################
ecoli.add_nucleotide_sequences(nt_sequences)
ecoli.add_essentials(essentials=essentials,
aa_dict=aa_dict,
rna_nucleotides=rna_nucleotides,
rna_nucleotides_mp=rna_nucleotides_mp)
ecoli.add_mrnas(mrna_dict.values())
ecoli.add_ribosome(rib, free_ratio=0.2)
# http://bionumbers.hms.harvard.edu/bionumber.aspx?id=102348&ver=1&trm=rna%20polymerase%20half%20life&org=
# Name Fraction of active RNA Polymerase
# Bionumber ID 102348
# Value 0.17-0.3 unitless
# Source Bremer, H., Dennis, P. P. (1996) Modulation of chemical composition and other parameters of the cell by growth rate.
# Neidhardt, et al. eds. Escherichia coli and Salmonella typhimurium: Cellular
# and Molecular Biology, 2nd ed. chapter 97 Table 1
ecoli.add_rnap(rnap, free_ratio=0.75)
ecoli.build_expression()
ecoli.add_enzymatic_coupling(coupling_dict)
if has_neidhardt:
nt_ratios, aa_ratios = get_ratios()
chromosome_len, gc_ratio = get_ecoli_gen_stats()
kdeg_mrna, mrna_length_avg = get_mrna_metrics()
kdeg_enz, peptide_length_avg = get_enz_metrics()
neidhardt_mu, neidhardt_rrel, neidhardt_prel, neidhardt_drel = get_neidhardt_data(
)
ecoli.add_interpolation_variables()
ecoli.add_dummies(nt_ratios=nt_ratios,
mrna_kdeg=kdeg_mrna,
mrna_length=mrna_length_avg,
aa_ratios=aa_ratios,
enzyme_kdeg=kdeg_enz,
peptide_length=peptide_length_avg)
ecoli.add_protein_mass_requirement(neidhardt_mu, neidhardt_prel)
ecoli.add_rna_mass_requirement(neidhardt_mu, neidhardt_rrel)
ecoli.add_dna_mass_requirement(mu_values=neidhardt_mu,
dna_rel=neidhardt_drel,
gc_ratio=gc_ratio,
chromosome_len=chromosome_len,
dna_dict=dna_nucleotides)
# Need to put after, because dummy has to be taken into account if used.
ecoli.populate_expression()
ecoli.add_trna_mass_balances()
ecoli.print_info()
need_relax = False
ecoli.repair()
if optimize:
try:
ecoli.optimize()
print('Objective : {}'.format(
ecoli.solution.objective_value))
print(' - Glucose uptake : {}'.format(
ecoli.reactions.EX_glc__D_e.flux))
print(' - Growth : {}'.format(
ecoli.growth_reaction.flux))
print(' - Ribosomes produced: {}'.format(ecoli.ribosome.X))
print(' - RNAP produced: {}'.format(ecoli.rnap.X))
except (AttributeError, SolverError):
pass
return ecoli
#biomass_rxn = 'BIOMASS_Ec_iJO1366_WT_53p95M'
biomass_rxn = 'Ec_biomass_iJO1366_WT_53p95M'
# We import pre-compiled data as it is faster for bigger models
model_path = '/projectnb2/bioinfor/SEGRE/goldford/CoenzymeSpecificity/pytfa/models'
cobra_model = load_json_model(model_path + '/iJO1366_NAD_ratio_1.fromTFA.json')
lexicon = read_lexicon(model_path + '/iJO1366/lexicon.csv')
compartment_data = read_compartment_data(model_path +
'/iJO1366/compartment_data.json')
# Initialize the cobra_model
mytfa = pytfa.ThermoModel(thermo_data, cobra_model)
# Annotate the cobra_model
annotate_from_lexicon(mytfa, lexicon)
apply_compartment_data(mytfa, compartment_data)
mytfa.name = 'iJO1366[NAD]'
mytfa.solver = solver
mytfa.objective = biomass_rxn
# Solver settings
def apply_solver_settings(model, solver=solver):
model.solver = solver
# model.solver.configuration.verbosity = 1
model.solver.configuration.tolerances.feasibility = 1e-9
if solver == 'optlang_gurobi':
model.solver.problem.Params.NumericFocus = 3
model.solver.configuration.presolve = True
def create_etfl_model(
has_thermo,
has_neidhardt,
n_mu_bins=64,
mu_max=3,
optimize=True,
solver=DEFAULT_SOLVER,
):
#------------------------------------------------------------
# Initialisation
#------------------------------------------------------------
# this hack works because we are using the solver switch to update the var
# names in the solver but really we should not do this
# TODO: clean up model.sanitize_varnames
growth_reaction_id = 'Biomass_Ecoli_core'
vanilla_model = create_fba_model()
vanilla_model.objective = growth_reaction_id
fba_sol = vanilla_model.slim_optimize()
mu_0 = fba_sol
mu_range = [0, mu_max]
n_mu_bins = n_mu_bins
coupling_dict = get_coupling_dict(vanilla_model,
mode='kmax',
atps_name='ATPS4r')
# coupling_dict = get_lloyd_coupling_dict(vanilla_model)
# Initialize the model
name = 'small_model_T{:1}E{:1}N{:1}_{}_enz_{}_bins.json'.format(
has_thermo, True, has_neidhardt, len(coupling_dict), n_mu_bins)
# for k,v in coupling_dict.items():
# for enz in v:
# enz.kcat_fwd = enz.kcat_bwd = 1e9
if has_thermo:
thermo_data, lexicon, compartment_data = get_thermo_data()
ecoli = ThermoMEModel(
thermo_data,
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
else:
ecoli = MEModel(
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
ecoli.name = name
ecoli.logger.setLevel(logging.WARNING)
ecoli.sloppy = True
# Solver settings
ecoli.solver = solver
standard_solver_config(ecoli)
if has_thermo:
# Annotate the cobra_model
annotate_from_lexicon(ecoli, lexicon)
apply_compartment_data(ecoli, compartment_data)
# TFA conversion
ecoli.prepare()
ecoli.convert() #add_displacement = True)
nt_sequences = get_nt_sequences()
mrna_dict = get_mrna_dict(ecoli)
rnap = get_rnap()
rib = get_rib()
all_peptides = set([
x for enzymes in coupling_dict.values() for enz in enzymes
for x in enz.composition
])
prune_to_genes = lambda the_dict:{k:v for k,v in the_dict.items() \
if k in vanilla_model.genes or
k in rib.rrna_composition or
k in rib.composition or
k in rnap.composition or
k in all_peptides}
nt_sequences = prune_to_genes(nt_sequences)
mrna_dict = prune_to_genes(mrna_dict)
# Remove nucleotides and amino acids from biomass reaction as they will be
# taken into account by the expression
remove_from_biomass_equation(model=ecoli,
nt_dict=rna_nucleotides,
aa_dict=aa_dict,
essentials_dict=essentials)
##########################
## MODEL CREATION ##
##########################
ecoli.add_nucleotide_sequences(nt_sequences)
ecoli.add_essentials(essentials=essentials,
aa_dict=aa_dict,
rna_nucleotides=rna_nucleotides,
rna_nucleotides_mp=rna_nucleotides_mp)
ecoli.add_mrnas(mrna_dict.values())
ecoli.add_ribosome(rib, 0.2)
ecoli.add_rnap(rnap, 0.75)
ecoli.build_expression()
ecoli.add_enzymatic_coupling(coupling_dict)
nt_ratios, aa_ratios = get_ratios()
chromosome_len, gc_ratio = get_ecoli_gen_stats()
kdeg_mrna, mrna_length_avg = get_mrna_metrics()
kdeg_enz, peptide_length_avg = get_enz_metrics()
ecoli.add_dummies(nt_ratios=nt_ratios,
mrna_kdeg=kdeg_mrna,
mrna_length=mrna_length_avg,
aa_ratios=aa_ratios,
enzyme_kdeg=kdeg_enz,
peptide_length=peptide_length_avg)
if has_neidhardt:
neidhardt_mu, neidhardt_rrel, neidhardt_prel, neidhardt_drel = get_neidhardt_data(
)
add_protein_mass_requirement(ecoli, neidhardt_mu, neidhardt_prel)
add_rna_mass_requirement(ecoli, neidhardt_mu, neidhardt_rrel)
add_dna_mass_requirement(ecoli,
mu_values=neidhardt_mu,
dna_rel=neidhardt_drel,
gc_ratio=gc_ratio,
chromosome_len=chromosome_len,
dna_dict=dna_nucleotides)
# Need to put after, because dummy has to be taken into account if used.
ecoli.populate_expression()
ecoli.add_trna_mass_balances()
ecoli.print_info()
need_relax = False
### a problem with the solver, which can be temporarily solved
ecoli.constraints.MB_b3855.set_linear_coefficients(
{ecoli.variables.b3855_degradation: -1e-5})
ecoli.repair()
if optimize:
try:
ecoli.optimize()
print('Objective : {}'.format(
ecoli.solution.objective_value))
print(' - Glucose uptake : {}'.format(
ecoli.reactions.EX_glc__D_e.flux))
print(' - Growth : {}'.format(
ecoli.growth_reaction.flux))
print(' - Ribosomes produced: {}'.format(ecoli.ribosome.X))
print(' - RNAP produced: {}'.format(ecoli.rnap.X))
except (AttributeError, SolverError):
pass
return ecoli
def create_model(has_thermo,
has_expression,
has_allocation,
kcat_mode='kmax',
infer_missing_enz=False,
additional_enz=None,
free_rib_ratio=0.2,
free_rnap_ratio=0.75,
add_displacement=False,
n_mu_bins=128,
name_suffix='',
kcat_overrides=None):
#------------------------------------------------------------
# Initialisation
#------------------------------------------------------------
assert has_expression == True
# this hack works because we are using the solver switch to update the var
# names in the solver but really we should not do this
# TODO: clean up model.sanitize_varnames
vanilla_model = get_model('optlang-glpk')
vanilla_model.reactions.EX_glc__D_e.lower_bound = -1 * glc_uptake - glc_uptake_std
vanilla_model.reactions.EX_glc__D_e.upper_bound = -1 * glc_uptake + glc_uptake_std
vanilla_model.objective = growth_reaction_id
fba_sol = vanilla_model.slim_optimize()
# vanilla_model.reactions.get_by_id(growth_reaction_id).lower_bound = observed_growth
# fva = flux_variability_analysis(vanilla_model)
# vanilla_model.reactions.get_by_id(growth_reaction_id).lower_bound = 0
# original_bounds = pd.DataFrame.from_dict(
# {r.id:(r.lower_bound, r.upper_bound)
# for r in vanilla_model.reactions}, orient = 'index')
# original_bounds.columns = ['lb','ub']
mu_0 = fba_sol
mu_range = [0, 3.5]
n_mu_bins = n_mu_bins
time_str = get_timestr()
coupling_dict = get_coupling_dict(vanilla_model,
mode=kcat_mode,
atps_name='ATPS4rpp',
infer_missing_enz=infer_missing_enz)
if additional_enz is not None:
additional_dict = get_transporters_coupling(
model=vanilla_model, additional_enz=additional_enz)
additional_dict.update(coupling_dict)
coupling_dict = additional_dict
if kcat_overrides is not None:
for rxn, enz_list in coupling_dict.items():
for e in enz_list:
if e.id in kcat_overrides:
prev_kcat = e.kcat_fwd
new_kcat = kcat_overrides[e.id]
e.kcat_fwd = new_kcat
e.kcat_bwd = new_kcat
print('Replaced kcat for {}: {} <-- {} s-1'.format(
e.id, prev_kcat / 3600, new_kcat / 3600))
aa_dict, rna_nucleotides, rna_nucleotides_mp, dna_nucleotides = get_monomers_dict(
)
essentials = get_essentials()
# Initialize the model
model_name = 'ETFL' if has_thermo else 'EFL'
model_name = ('v' + model_name) if has_allocation else model_name
model_name = (model_name +
'_{}'.format(name_suffix)) if name_suffix else model_name
model_name = (model_name +
'_infer') if bool(infer_missing_enz) else model_name
name = 'iJO1366_{}_{}_enz_{}_bins_{}.json'.format(model_name,
len(coupling_dict),
n_mu_bins, time_str)
if has_thermo:
thermo_data, lexicon, compartment_data = get_thermo_data()
ecoli = ThermoMEModel(
thermo_data,
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
else:
ecoli = MEModel(
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
ecoli.name = name
ecoli.logger.setLevel(logging.WARNING)
ecoli.sloppy = True
# apply_bounds(ecoli,fva)
ecoli.solver = solver
standard_solver_config(ecoli, verbose=False)
if has_thermo:
# Annotate the cobra_model
annotate_from_lexicon(ecoli, lexicon)
apply_compartment_data(ecoli, compartment_data)
# TFA conversion
ecoli.prepare()
ecoli.convert(add_displacement=add_displacement)
mrna_dict = get_mrna_dict(ecoli)
nt_sequences = get_nt_sequences()
rnap = get_rnap()
# rnap.kcat_fwd *= 0.5
rib = get_rib()
# Remove nucleotides and amino acids from biomass reaction as they will be
# taken into account by the expression
remove_from_biomass_equation(model=ecoli,
nt_dict=rna_nucleotides,
aa_dict=aa_dict,
essentials_dict=essentials)
##########################
## MODEL CREATION ##
##########################
ecoli.add_nucleotide_sequences(nt_sequences)
ecoli.add_essentials(essentials=essentials,
aa_dict=aa_dict,
rna_nucleotides=rna_nucleotides,
rna_nucleotides_mp=rna_nucleotides_mp)
ecoli.add_mrnas(mrna_dict.values())
ecoli.add_ribosome(rib, free_rib_ratio)
ecoli.add_rnap(rnap, free_rnap_ratio)
ecoli.build_expression()
ecoli.add_enzymatic_coupling(coupling_dict)
if has_allocation:
nt_ratios, aa_ratios = get_ratios()
chromosome_len, gc_ratio = get_ecoli_gen_stats()
kdeg_mrna, mrna_length_avg = get_mrna_metrics()
kdeg_enz, peptide_length_avg = get_enz_metrics()
neidhardt_mu, neidhardt_rrel, neidhardt_prel, neidhardt_drel = get_neidhardt_data(
)
ecoli.add_dummies(nt_ratios=nt_ratios,
mrna_kdeg=kdeg_mrna,
mrna_length=mrna_length_avg,
aa_ratios=aa_ratios,
enzyme_kdeg=kdeg_enz,
peptide_length=peptide_length_avg)
add_protein_mass_requirement(ecoli, neidhardt_mu, neidhardt_prel)
add_rna_mass_requirement(ecoli, neidhardt_mu, neidhardt_rrel)
add_dna_mass_requirement(ecoli,
mu_values=neidhardt_mu,
dna_rel=neidhardt_drel,
gc_ratio=gc_ratio,
chromosome_len=chromosome_len,
dna_dict=dna_nucleotides)
dna_pol = get_dna_polymerase()
ecoli.add_enzymatic_coupling({'DNA_formation': [
dna_pol,
]})
# Need to put after, because dummy has to be taken into account if used.
ecoli.populate_expression()
ecoli.add_trna_mass_balances()
ecoli.print_info()
ecoli.growth_reaction.lower_bound = observed_growth - 1 * observed_growth_std
need_relax = False
ecoli.repair()
try:
ecoli.optimize()
except (AttributeError, SolverError):
need_relax = True
if has_thermo and need_relax:
# final_model, slack_model, relax_table = relax_dgo(ecoli)
final_model, slack_model, relax_table = relax_dgo(ecoli, in_place=True)
else:
final_model = ecoli
final_model.growth_reaction.lower_bound = 0
# apply_bounds(ecoli, original_bounds)
solution = final_model.optimize()
print_standard_sol(final_model)
filepath = 'models/{}'.format(final_model.name)
save_json_model(final_model, filepath)
final_model.logger.info('Build complete for model {}'.format(
final_model.name))
return final_model
def __init__(self,
model_code='ecoli:iJO1366',
solver='gurobi',
min_biomass=0.55):
start_time = time.time()
super().__init__(model_code, solver, min_biomass)
if self.species == 'ecoli':
# Add cystein -> selenocystein transformation for convenience
selcys = Metabolite(id='selcys__L_c',
compartment='c',
formula='C3H7NO2Se')
selcys_rxn = Reaction(id='PSEUDO_selenocystein_synthase',
name='PSEUDO Selenocystein_Synthase')
selcys_rxn.add_metabolites({
self.model.metabolites.cys__L_c: -1,
selcys: +1
})
self.model.add_reactions([selcys_rxn])
self._sanitize_varnames()
# self.model.reactions.EX_glc__D_e.lower_bound = -1 * glc_uptake - glc_uptake_std
# self.model.reactions.EX_glc__D_e.upper_bound = -1 * glc_uptake + glc_uptake_std
# time_str = get_timestr()
coupling_dict = get_coupling_dict(self.model,
mode='kmax',
atps_name='ATPS4rpp',
infer_missing_enz=True)
aa_dict, rna_nucleotides, rna_nucleotides_mp, dna_nucleotides = get_monomers_dict(
)
essentials = get_essentials()
# if has_thermo:
thermo_db = load_thermoDB(
join(data_dir, 'thermo/thermo_data.thermodb'))
self.model = ThermoMEModel(thermo_db,
model=self.model,
growth_reaction=self.biomass_reaction,
mu_range=mu_range,
n_mu_bins=n_mu_bins)
self.model.name = self.model_name
# annotate_from_lexicon(self.model, read_lexicon(dir_path + '/data/thermo/lexicon.csv'))
# compartment_data = read_compartment_data(dir_path + '/data/thermo/compartment_data.json')
# apply_compartment_data(self.model, compartment_data)
apply_compartment_data(
self.model,
read_compartment_data(
join(data_dir, 'thermo/compartment_data.json')))
annotate_from_lexicon(
self.model, read_lexicon(join(data_dir, 'thermo/lexicon.csv')))
self.model.prepare()
# self.model.reactions.MECDPS.thermo['computed'] = False
# self.model.reactions.NDPK4.thermo['computed'] = False
# self.model.reactions.TMDPP.thermo['computed'] = False
# self.model.reactions.ARGAGMt7pp.thermo['computed'] = False
self.model.convert()
# else:
# self.model = MEModel(model=self.model, growth_reaction=growth_reaction_id, mu_range=mu_range,
# n_mu_bins=n_mu_bins, name=name)
# mrna_dict = get_mrna_dict(self.model)
# nt_sequences = get_nt_sequences()
nt_sequences = pd.read_csv(join(
data_dir, f'{self.species}/{self.model_name}_nt_seq_kegg.csv'),
index_col=0,
header=None).iloc[:, 0]
mrna_dict = self.get_mrna_dict(nt_sequences)
rnap = get_rnap()
rib = get_rib()
# Remove nucleotides and amino acids from biomass reaction as they will be
# taken into account by the expression
remove_from_biomass_equation(model=self.model,
nt_dict=rna_nucleotides,
aa_dict=aa_dict,
essentials_dict=essentials)
self.model.add_nucleotide_sequences(nt_sequences)
self.model.add_essentials(essentials=essentials,
aa_dict=aa_dict,
rna_nucleotides=rna_nucleotides,
rna_nucleotides_mp=rna_nucleotides_mp)
self.model.add_mrnas(mrna_dict.values())
self.model.add_ribosome(rib, free_ratio=0.2)
# http://bionumbers.hms.harvard.edu/bionumber.aspx?id=102348&ver=1&trm=rna%20polymerase%20half%20life&org=
# Name Fraction of active RNA Polymerase
# Bionumber ID 102348
# Value 0.17-0.3 unitless
# Source Bremer, H., Dennis, P. P. (1996) Modulation of chemical composition and other parameters of the cell by growth rate.
# Neidhardt, et al. eds. Escherichia coli and Salmonella typhimurium: Cellular
# and Molecular Biology, 2nd ed. chapter 97 Table 1
self.model.add_rnap(rnap, free_ratio=0.75)
self.model.build_expression()
self.model.add_enzymatic_coupling(coupling_dict)
# if has_neidhardt:
# nt_ratios, aa_ratios = get_ratios()
# chromosome_len, gc_ratio = get_ecoli_gen_stats()
# kdeg_mrna, mrna_length_avg = get_mrna_metrics()
# kdeg_enz, peptide_length_avg = get_enz_metrics()
# neidhardt_mu, neidhardt_rrel, neidhardt_prel, neidhardt_drel = get_neidhardt_data()
#
# add_interpolation_variables(self.model)
# self.model.add_dummies(nt_ratios=nt_ratios, mrna_kdeg=kdeg_mrna, mrna_length=mrna_length_avg,
# aa_ratios=aa_ratios, enzyme_kdeg=kdeg_enz, peptide_length=peptide_length_avg)
# add_protein_mass_requirement(self.model, neidhardt_mu, neidhardt_prel)
# add_rna_mass_requirement(self.model, neidhardt_mu, neidhardt_rrel)
# add_dna_mass_requirement(self.model, mu_values=neidhardt_mu, dna_rel=neidhardt_drel, gc_ratio=gc_ratio,
# chromosome_len=chromosome_len, dna_dict=dna_nucleotides)
# Need to put after, because dummy has to be taken into account if used.
self.model.populate_expression()
self.model.add_trna_mass_balances()
# self.model.growth_reaction.lower_bound = objective_lb
self.model.repair()
print(
f"Building ETFL model costs {time.time() - start_time:.2f} seconds!"
)
try:
start_time = time.time()
self.model.optimize()
except (AttributeError, SolverError):
print(
f"Solving no relaxed model costs {time.time() - start_time:.2f} seconds!"
)
start_time = time.time()
self.model, _, _ = relax_dgo(self.model, in_place=True)
print(
f"Relaxing model costs {time.time() - start_time:.2f} seconds!"
)
# self.model.growth_reaction.lower_bound = 0
# print(f"Build ETFL model for {time.time() - start_time:.2f} seconds!")
self.model.print_info()
'models/iJO1366/compartment_data.json')
# Scaling to avoid numerical errors with bad lumps
for rxn in model.reactions:
if rxn.id.startswith('LMPD_'):
rxn.add_metabolites(
{x: v * (0.1 - 1)
for x, v in rxn.metabolites.items()})
thermo_data = load_thermoDB(thermoDB)
lexicon = read_lexicon(path_to_lexicon)
compartment_data = read_compartment_data(path_to_compartment_data)
tfa_model = ThermoModel(thermo_data, model)
annotate_from_lexicon(tfa_model, lexicon)
apply_compartment_data(tfa_model, compartment_data)
tfa_model.name = 'Lumped Model'
tfa_model.prepare()
tfa_model.convert()
# tfa_model.solver.configuration.verbosity = True
tfa_model.logger.setLevel = 30
def test_redgem():
redgem = RedGEM(tfa_model, path_to_params, False)
rgem = redgem.run()
obj_val = rgem.slim_optimize()
# assert(obj_val > 0)
return rgem, redgem
def create_model(has_thermo, has_expression, has_neidhardt, n_mu_bins=128):
#------------------------------------------------------------
# Initialisation
#------------------------------------------------------------
assert has_expression == True
# this hack works because we are using the solver switch to update the var
# names in the solver but really we should not do this
# TODO: clean up model.sanitize_varnames
vanilla_model = get_model('optlang-glpk')
vanilla_model.reactions.EX_glc__D_e.lower_bound = -1 * glc_uptake - glc_uptake_std
vanilla_model.reactions.EX_glc__D_e.upper_bound = -1 * glc_uptake + glc_uptake_std
vanilla_model.objective = growth_reaction_id
fba_sol = vanilla_model.slim_optimize()
mu_0 = fba_sol
mu_range = [0, 3.5]
n_mu_bins = n_mu_bins
time_str = get_timestr()
coupling_dict = get_coupling_dict(
vanilla_model,
mode='kmax',
# mode = 'kcat',
atps_name='ATPS4rpp',
infer_missing_enz=True)
# coupling_dict = get_lloyd_coupling_dict(vanilla_model)
aa_dict, rna_nucleotides, rna_nucleotides_mp, dna_nucleotides = get_monomers_dict(
)
essentials = get_essentials()
# Initialize the model
name = 'iJO1366_T{:1}E{:1}N{:1}_{}_enz_{}_bins_{}.json'.format(
has_thermo, has_expression, has_neidhardt, len(coupling_dict),
n_mu_bins, time_str)
if has_thermo:
thermo_data, lexicon, compartment_data = get_thermo_data()
ecoli = ThermoMEModel(
thermo_data,
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
else:
ecoli = MEModel(
model=vanilla_model,
growth_reaction=growth_reaction_id,
mu_range=mu_range,
n_mu_bins=n_mu_bins,
name=name,
)
ecoli.name = name
ecoli.logger.setLevel(logging.WARNING)
ecoli.solver = solver
standard_solver_config(ecoli)
if has_thermo:
# Annotate the cobra_model
annotate_from_lexicon(ecoli, lexicon)
apply_compartment_data(ecoli, compartment_data)
# TFA conversion
ecoli.prepare()
# ecoli.reactions.GLUDy.thermo['computed'] = False
# ecoli.reactions.DHAtpp.thermo['computed'] = False
# ecoli.reactions.MLTP2.thermo['computed'] = False
# ecoli.reactions.G3PD2.thermo['computed'] = False
ecoli.reactions.MECDPS.thermo['computed'] = False
ecoli.reactions.NDPK4.thermo['computed'] = False
ecoli.reactions.TMDPP.thermo['computed'] = False
ecoli.reactions.ARGAGMt7pp.thermo['computed'] = False
ecoli.convert() #add_displacement = True)
mrna_dict = get_mrna_dict(ecoli)
nt_sequences = get_nt_sequences()
rnap = get_rnap()
rib = get_rib()
# Remove nucleotides and amino acids from biomass reaction as they will be
# taken into account by the expression
remove_from_biomass_equation(
model=ecoli,
nt_dict=rna_nucleotides,
aa_dict=aa_dict,
atp_id=essentials['atp'],
adp_id=essentials['adp'],
pi_id=essentials['pi'],
h2o_id=essentials['h2o'],
h_id=essentials['h'],
)
##########################
## MODEL CREATION ##
##########################
ecoli.add_nucleotide_sequences(nt_sequences)
ecoli.add_essentials(essentials=essentials,
aa_dict=aa_dict,
rna_nucleotides=rna_nucleotides,
rna_nucleotides_mp=rna_nucleotides_mp)
ecoli.add_mrnas(mrna_dict.values())
ecoli.add_ribosome(rib, free_ratio=0.2)
# http://bionumbers.hms.harvard.edu/bionumber.aspx?id=102348&ver=1&trm=rna%20polymerase%20half%20life&org=
# Name Fraction of active RNA Polymerase
# Bionumber ID 102348
# Value 0.17-0.3 unitless
# Source Bremer, H., Dennis, P. P. (1996) Modulation of chemical composition and other parameters of the cell by growth rate.
# Neidhardt, et al. eds. Escherichia coli and Salmonella typhimurium: Cellular
# and Molecular Biology, 2nd ed. chapter 97 Table 1
ecoli.add_rnap(rnap, free_ratio=0.75)
ecoli.build_expression()
ecoli.add_enzymatic_coupling(coupling_dict)
if has_neidhardt:
nt_ratios, aa_ratios = get_ratios()
chromosome_len, gc_ratio = get_ecoli_gen_stats()
kdeg_mrna, mrna_length_avg = get_mrna_metrics()
kdeg_enz, peptide_length_avg = get_enz_metrics()
neidhardt_mu, neidhardt_rrel, neidhardt_prel, neidhardt_drel = get_neidhardt_data(
)
ecoli.add_interpolation_variables()
ecoli.add_dummies(nt_ratios=nt_ratios,
mrna_kdeg=kdeg_mrna,
mrna_length=mrna_length_avg,
aa_ratios=aa_ratios,
enzyme_kdeg=kdeg_enz,
peptide_length=peptide_length_avg)
ecoli.add_protein_mass_requirement(neidhardt_mu, neidhardt_prel)
ecoli.add_rna_mass_requirement(neidhardt_mu, neidhardt_rrel)
ecoli.add_dna_mass_requirement(mu_values=neidhardt_mu,
dna_rel=neidhardt_drel,
gc_ratio=gc_ratio,
chromosome_len=chromosome_len,
dna_dict=dna_nucleotides)
# Need to put after, because dummy has to be taken into account if used.
ecoli.populate_expression()
ecoli.add_trna_mass_balances()
ecoli.print_info()
ecoli.growth_reaction.lower_bound = observed_growth - 3 * observed_growth_std
need_relax = False
ecoli.repair()
try:
ecoli.optimize()
except (AttributeError, SolverError):
need_relax = True
# from ipdb import set_trace; set_trace()
if has_thermo and need_relax:
final_model, slack_model, relax_table = relax_dgo(ecoli)
# final_model, slack_model, relax_table = relax_dgo(ecoli, in_place = True)
else:
final_model = ecoli
final_model.growth_reaction.lower_bound = 0
solution = final_model.optimize()
print('Objective : {}'.format(
final_model.solution.objective_value))
print(' - Glucose uptake : {}'.format(
final_model.reactions.EX_glc__D_e.flux))
print(' - Growth : {}'.format(final_model.growth_reaction.flux))
print(' - Ribosomes produced: {}'.format(final_model.ribosome.X))
print(' - RNAP produced: {}'.format(final_model.rnap.X))
try:
print(' - DNA produced: {}'.format(final_model.solution.raw.DN_DNA))
except AttributeError:
pass
filepath = 'models/{}'.format(final_model.name)
# save_json_model(final_model, filepath)
final_model.logger.info('Build complete for model {}'.format(
final_model.name))
return final_model
# ------------------------------------------------------------
# Thermo
# ------------------------------------------------------------
def curate_lexicon(lexicon):
ix = pd.Series(lexicon.index)
ix = ix.apply(lambda s: str.replace(s,'-','__'))
ix = ix.apply(lambda s: '_'+s if s[0].isdigit() else s)
lexicon.index = ix
return lexicon
lexicon = curate_lexicon(read_lexicon('thermo_data/iJO1366_lexicon.csv'))
# Annotate the cobra_model
annotate_from_lexicon(ecoli, lexicon)
apply_compartment_data(ecoli, compartment_data)
# TFA conversion
ecoli.prepare()
ecoli.convert()#add_displacement = True)
#------------------------------------------------------------
# Data
#------------------------7.54-------------------------------------
# Growth-related abundances
neidhardt_data = pd.read_excel(pjoin(data_dir,'neidhardt_tab2.xlsx'),
skiprows=range(0,6),
skip_footer=22)
def create_tfa_model(add_displacement=False):
#------------------------------------------------------------
# Initialisation
#------------------------------------------------------------
time_str = get_timestr()
name = 'iJO1366_TFA_{}.json'.format(time_str)
# this hack works because we are using the solver switch to update the var
# names in the solver but really we should not do this
# TODO: clean up model.sanitize_varnames
vanilla_model = get_model('optlang-glpk')
vanilla_model.reactions.EX_glc__D_e.lower_bound = -1 * glc_uptake - glc_uptake_std
vanilla_model.reactions.EX_glc__D_e.upper_bound = -1 * glc_uptake + glc_uptake_std
vanilla_model.objective = growth_reaction_id
fba_sol = vanilla_model.slim_optimize()
thermo_data, lexicon, compartment_data = get_thermo_data()
ecoli = ThermoModel(
thermo_data=thermo_data,
model=vanilla_model,
name=name,
)
ecoli.name = name
ecoli.logger.setLevel(logging.WARNING)
ecoli.sloppy = True
# apply_bounds(ecoli,fva)
ecoli.solver = solver
annotate_from_lexicon(ecoli, lexicon)
apply_compartment_data(ecoli, compartment_data)
# TFA conversion
ecoli.prepare()
ecoli.convert(add_displacement=add_displacement)
ecoli.print_info()
ecoli.reactions.get_by_id(growth_reaction_id).lower_bound = observed_growth - \
1*observed_growth_std
need_relax = False
ecoli.repair()
try:
ecoli.optimize()
except (AttributeError, SolverError):
need_relax = True
if need_relax:
# final_model, slack_model, relax_table = relax_dgo(ecoli)
final_model, slack_model, relax_table = relax_dgo(ecoli, in_place=True)
else:
final_model = ecoli
final_model.reactions.get_by_id(growth_reaction_id).lower_bound = 0
# apply_bounds(ecoli, original_bounds)
solution = final_model.optimize()
print('Objective : {}'.format(
final_model.solution.objective_value))
print(' - Glucose uptake : {}'.format(
final_model.reactions.EX_glc__D_e.flux))
filepath = 'models/{}'.format(final_model.name)
save_json_model(final_model, filepath)
final_model.logger.info('Build complete for model {}'.format(
final_model.name))
return final_model
