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
def make_thermo_model(): vanilla_model = get_model(solver) name = 'iJO1366_T1E0N0_{}'.format(get_timestr()) 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 vanilla_model.optimize() thermo_data, lexicon, compartment_data = get_thermo_data() ecoli = ThermoMEModel( thermo_data, model=vanilla_model, name=name, ) need_relax = False try: ecoli.optimize() except AttributeError: need_relax = True if need_relax: final_model, slack_model, relax_table = relax_dgo(ecoli) else: final_model = ecoli from pytfa.io.json import save_json_model save_json_model(final_model, 'models/' + name)
def make_fba_model(): ecoli = get_model(solver) ecoli.reactions.EX_glc__D_e.lower_bound = -1 * glc_uptake - glc_uptake_std ecoli.reactions.EX_glc__D_e.upper_bound = -1 * glc_uptake + glc_uptake_std # ecoli.objective = growth_reaction_id ecoli.optimize() from cobra.io.json import save_json_model save_json_model(ecoli, 'models/iJO1366_T0E0N0_{}.json'.format(get_timestr()))
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 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
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