def isotopomer_model_iteration1():
'''iteration 1:
identification of reactions that can be lumped in pathways outside the model scope'''
cobra_model = load_ALEWt();
# Make the model irreversible for downstream manipulations:
convert_to_irreversible(cobra_model);
# Add lumped isotopomer reactions
add_net_reaction(cobra_model,isotopomer_rxns_net_irreversible);
# Find minimal flux solution:
pfba = optimize_minimal_flux(cobra_model,True,solver='gurobi');
# Write pfba solution to file
with open('data/iteration1_140407_ijo1366_reduced_modified_pfba.csv',mode='wb') as outfile:
writer = csv.writer(outfile)
writer.writerow(['Reaction','Flux'])
for k,v in cobra_model.solution.x_dict.items():
writer.writerow([k,v]);
# Read in pfba solution
pfba_sol = {};
with open('data/iteration1_140407_ijo1366_reduced_modified_pfba.csv',mode='r') as infile:
dictreader = csv.DictReader(infile)
for r in dictreader:
pfba_sol[r['Reaction']] = r['Flux'];
# Make net reactions for pathways outside of the scope
# of the isotopomer model
subs = ['Cell Envelope Biosynthesis',
'Glycerophospholipid Metabolism',
'Lipopolysaccharide Biosynthesis / Recycling',
'Membrane Lipid Metabolism',
'Murein Biosynthesis'
'Murein Recycling',
'Cofactor and Prosthetic Group Biosynthesis',
#'Transport, Inner Membrane',
#'Transport, Outer Membrane',
#'Transport, Outer Membrane Porin',
'tRNA Charging',
'Unassigned',
'Exchange',
'Inorganic Ion Transport and Metabolism',
'Nitrogen Metabolism'];
add_net_reaction_subsystem(cobra_model,pfba_sol,subs);
remove_noflux_reactions(cobra_model,pfba_sol,['Transport, Outer Membrane Porin','Transport, Inner Membrane','Transport, Outer Membrane'])
revert_to_reversible(cobra_model);
# write model to sbml
write_cobra_model_to_sbml_file(cobra_model,'data/iteration1_140407_ijo1366_netrxn_irreversible.xml')
# Reduce model using FVA:
reduce_model(cobra_model,"data/iteration1_140407_ijo1366_reduced.xml")
# Remove all reactions with 0 flux
remove_noflux_reactions(cobra_model);
with open('data/iteration1_140407_ijo1366_reduced_netrxn_lbub.csv',mode='wb') as outfile:
writer = csv.writer(outfile)
writer.writerow(['Reaction','Formula','LB','UB','Subsystem'])
for r in cobra_model.reactions:
writer.writerow([r.id,
r.build_reaction_string(),
r.lower_bound,
r.upper_bound,
r.subsystem]);
def test_modify_reversible(self):
model1 = self.model
model1.optimize()
model2 = create_test_model()
modify.convert_to_irreversible(model2)
model2.optimize()
self.assertAlmostEqual(model1.solution.f, model2.solution.f, places=3)
modify.revert_to_reversible(model2)
model2.optimize()
self.assertAlmostEqual(model1.solution.f, model2.solution.f, places=3)
def test_modify_reversible(self):
model1 = create_test_model("textbook")
model1.optimize()
model2 = create_test_model("textbook")
modify.convert_to_irreversible(model2)
model2.optimize()
self.assertAlmostEqual(model1.solution.f, model2.solution.f, places=3)
modify.revert_to_reversible(model2)
model2.optimize()
self.assertAlmostEqual(model1.solution.f, model2.solution.f, places=3)
# Ensure revert_to_reversible is robust to solutions generated both
# before and after reversibility conversion, or not solved at all.
model3 = create_test_model("textbook")
model3.optimize()
modify.convert_to_irreversible(model3)
modify.revert_to_reversible(model3)
self.assertAlmostEqual(model1.solution.f, model3.solution.f, places=3)
# test reaction where both bounds are negative
model4 = create_test_model("textbook")
glc = model4.reactions.get_by_id("EX_glc__D_e")
glc.upper_bound = -1
modify.convert_to_irreversible(model4)
model4.optimize()
self.assertAlmostEqual(model1.solution.f, model4.solution.f, places=3)
glc_rev = model4.reactions.get_by_id(glc.notes["reflection"])
self.assertEqual(glc_rev.lower_bound, 1)
self.assertEqual(glc.upper_bound, 0)
modify.revert_to_reversible(model4)
self.assertEqual(glc.upper_bound, -1)
def test_modify_reversible(self):
model1 = self.model
model1.optimize()
model2 = create_test_model()
modify.convert_to_irreversible(model2)
model2.optimize()
self.assertAlmostEqual(model1.solution.f, model2.solution.f, places=3)
modify.revert_to_reversible(model2)
model2.optimize()
self.assertAlmostEqual(model1.solution.f, model2.solution.f, places=3)
# Ensure revert_to_reversible is robust to solutions generated both
# before and after reversibility conversion, or not solved at all.
model3 = create_test_model()
model3.optimize()
modify.convert_to_irreversible(model3)
modify.revert_to_reversible(model3)
self.assertAlmostEqual(model1.solution.f, model3.solution.f, places=3)
model4 = create_test_model()
modify.convert_to_irreversible(model4)
modify.revert_to_reversible(model4)
copies_fL = pd.read_csv("../data/abundance[copies_fl].csv")
copies_fL = copies_fL[['bnumber', 'GLC_BATCH_mu=0.58_S']]
abundance = pd.DataFrame.from_csv("../data/g_gCDW.csv")
cu = CAPACITY_USAGE(flux, abundance)
uni_to_b = {row[48:54]:row[0:5].split(';')[0].strip()
for row in open("../data/all_ecoli_genes.txt", 'r')}
id_mapper = pd.DataFrame.from_dict(uni_to_b.items())
id_mapper.columns = ["uniprot", "bnumber"]
TS = pd.read_csv("../data/thermoal_stability_ecoli.csv")
df = TS.merge(id_mapper, on=["uniprot"])
#%%
model = cu.model.copy()
revert_to_reversible(model)
def one2one_mapping():
l = []
for b in cu.model.genes:
l+=(list(product([b.id], list(b.reactions))))
df = pd.DataFrame(l)
df.columns = ['bnumber', 'reaction']
df.loc[:, '# genes in reaction'] = [len(r.genes) for r in df['reaction']]
return df
b2r = one2one_mapping()
df = df.merge(b2r, on="bnumber", how='outer')
df = df.merge(copies_fL, on="bnumber", how='outer')
df['reaction'] = df['reaction'].map(str, )
kmax = cu.kmax
kmax.name='kmax'
def MergeRev(model, update_solution=True):
modify.revert_to_reversible(model, update_solution=update_solution)
