def remove_blocked_reactions(model):
epsilon = model.solver.configuration.tolerances.feasibility
# fva = flux_variability_analysis(model)
fva = variability_analysis(model, kind='reactions')
# Blocked reactions have max and min at 0
df = fva[ (fva.max(axis=1).abs()<1*epsilon)
& (fva.min(axis=1).abs()<1*epsilon)]
rid_to_rm = df.index
model.remove_reactions(rid_to_rm)
return df
## re-prepare the TFA model
m.prepare()
m.convert() #add_displacement = True)
## Info on the cobra_model
m.print_info()
tfa_solution_sc = m.optimize()
tfa_value_sc = tfa_solution_sc.objective_value
# Report
print('TFA Solution found for NAD-only metabolic model: {0:.5g}'.format(
tfa_value_sc))
# perform variability analysis on reactions
mytfa.reactions.get_by_id(biomass_rxn).lower_bound = tfa_value - 1e-6
varib_results = variability_analysis(mytfa, kind='reactions')
varib_results.to_csv(out_dir + '/FVA.wt.results.optimal.csv')
print("finished variability analysis on optimal solution for WT model")
# perform variability analysis on reactions
mytfa.reactions.get_by_id(biomass_rxn).lower_bound = 0.1
varib_results = variability_analysis(mytfa, kind='reactions')
varib_results.to_csv(out_dir + '/FVA.wt.results.gtzp1.csv')
print("finished variability analysis with growth rate = 0.1 for WT model")
# perform variability analysis on reactions
mytfa.reactions.get_by_id(biomass_rxn).lower_bound = 0.5
varib_results = variability_analysis(mytfa, kind='reactions')
print("finished variability analysis with growth rate = 0.5 for WT model")
varib_results.to_csv(out_dir + '/FVA.wt.results.gtzp5.csv')
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
if __name__ == '__main__':
tfa_model = create_tfa_model()
print('Completed')
from pytfa.analysis.variability import variability_analysis
tva = variability_analysis(tfa_model)
tva.to_csv('outputs/tva_iJO1366.csv')
# Make thermo model
# make_thermo_model()
# Save FBA model
# make_fba_model()