def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
simplify(model)
make_irreversible(model)
simplify(model) #remove directionally blocked reactions
model2 = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
make_irreversible(model2)
simplify(model2)
self.assertEqual(model.id, model2.id)
self.assertListEqual(list(model.metabolites.keys()),
list(model2.metabolites.keys()))
self.assertListEqual(list(model.reactions.keys()),
list(model2.reactions.keys()))
self.assertListEqual(list(model.genes.keys()),
list(model2.genes.keys()))
for r1, r2 in zip(list(model.reactions.values()),
list(model2.reactions.values())):
self.assertEqual(r1.name, r2.name)
self.assertEqual(r1.reversible, r2.reversible)
self.assertDictEqual(r1.stoichiometry, r2.stoichiometry)
self.assertEqual(r1.lb, r2.lb)
self.assertEqual(r1.ub, r2.ub)
self.assertEqual(str(r1.gpr), str(r2.gpr))
self.assertSetEqual(set(model.genes.keys()), set(model2.genes.keys()))
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
write_model_to_file(model, PLAIN_TEXT_COPY)
model_copy = read_model_from_file(PLAIN_TEXT_COPY, kind='cb')
solution = FBA(model_copy)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
make_irreversible(model)
self.assertTrue(all([not reaction.reversible for reaction in model.reactions.values()]))
solution = FBA(model)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
make_irreversible(model)
self.assertTrue(all([not reaction.reversible for reaction in model.reactions.values()]))
solution = FBA(model)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def reac_ko_medium_optim_CM(isMultiProc=False, size=(1, 1), withCobraPy=False):
SBML_FILE = basePath + "Data/EC_SC_model.xml"
modelaux = load_cbmodel(SBML_FILE, exchange_detection_mode="R_EX_")
model = fix_exchange_reactions_model(modelaux)
fileRes = basePath + "Results/optim_Comunity_KO_medium_pFBA.csv"
for r_id, rxn in model.reactions.items():
if r_id.startswith(
'R_EX_'): # ou tambem podes fazer if rxn.is_exchange:
rxn.lb = -1000 if rxn.lb is None else rxn.lb
rxn.ub = 1000 if rxn.ub is None else rxn.ub
simulProb = StoicSimulationProblem(model,
objective={"R_BM_total_Synth": 1},
withCobraPy=withCobraPy)
res = simulProb.simulate()
print(res.print())
evalFunc = build_evaluation_function("BP_MinModifications",
"R_BM_total_Synth",
"R_EX_succ_medium_")
result = cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM_REACTION_KO,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
write_model_to_file(model, PLAIN_TEXT_COPY)
model_copy = read_model_from_file(PLAIN_TEXT_COPY, kind='cb')
solution = FBA(model_copy)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
variability = FVA(model)
self.assertTrue(
all([
lb <= ub if lb is not None and ub is not None else True
for lb, ub in variability.values()
]))
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution = gene_deletion(model, DOUBLE_GENE_KO, 'pFBA')
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.values[model.biomass_reaction],
DOUBLE_KO_GROWTH_RATE, 2)
self.assertAlmostEqual(solution.values['R_EX_succ_e'],
DOUBLE_KO_SUCC_EX, 2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
r_id1 = 'R_PGI'
r_id2 = 'R_G6PDH2r'
ratio = 2.0
model.add_ratio_constraint(r_id1, r_id2, ratio)
solution = FBA(model, get_shadow_prices=True, get_reduced_costs=True)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertEqual(solution.values[r_id1] / solution.values[r_id2], ratio)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
r_id1 = 'R_PGI'
r_id2 = 'R_G6PDH2r'
ratio = 2.0
model.add_ratio_constraint(r_id1, r_id2, ratio)
solution = FBA(model, get_shadow_prices=True, get_reduced_costs=True)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertEqual(solution.values[r_id1] / solution.values[r_id2], ratio)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution1 = pFBA(model)
solution2 = FBA(model)
self.assertEqual(solution1.status, Status.OPTIMAL)
self.assertEqual(solution2.status, Status.OPTIMAL)
growth1 = solution1.values[model.biomass_reaction]
growth2 = solution2.values[model.biomass_reaction]
self.assertAlmostEqual(growth1, growth2, places=4)
norm1 = sum([abs(solution1.values[r_id]) for r_id in model.reactions])
norm2 = sum([abs(solution2.values[r_id]) for r_id in model.reactions])
self.assertLessEqual(norm1, norm2 + 1e-6)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution1 = pFBA(model)
solution2 = FBA(model)
self.assertEqual(solution1.status, Status.OPTIMAL)
self.assertEqual(solution2.status, Status.OPTIMAL)
growth1 = solution1.values[model.biomass_reaction]
growth2 = solution2.values[model.biomass_reaction]
self.assertAlmostEqual(growth1, growth2, places=4)
norm1 = sum([abs(solution1.values[r_id]) for r_id in model.reactions])
norm2 = sum([abs(solution2.values[r_id]) for r_id in model.reactions])
self.assertLessEqual(norm1, norm2 + 1e-6)
def medium_Lactate_optim(isMultiProc=False, size=1, withCobraPy=False):
SBML_FILE = "../../../examples/models/Ec_iAF1260.xml"
modelaux = load_cbmodel(SBML_FILE, flavor="cobra")
model = fix_exchange_reactions_model(modelaux)
fileRes = basePath + "Results/optim_Ec_iAF1260_medium_lactate.csv"
simulProb = StoicSimulationProblem(
model,
objective={"R_Ec_biomass_iAF1260_core_59p81M": 1},
withCobraPy=withCobraPy)
fba = simulProb.simulate()
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
criticalReacs = essential
# set the minimum production of biomass
simulProb.set_objective_function({"R_EX_lac_L_e": 1})
simulProb.constraints.update({
"R_Ec_biomass_iAF1260_core_59p81M":
(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.75, 9999)
})
minObjective = {"R_EX_lac_L_e": 1e-5}
print(
"Biomass: " +
str(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.75))
build_evaluation_function
evalFunc = build_evaluation_function("MinNumberReac", size, minObjective)
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM,
criticalReacs=criticalReacs,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
simplify(model)
make_irreversible(model)
simplify(model) #remove directionally blocked reactions
model2 = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
make_irreversible(model2)
simplify(model2)
self.assertEqual(model.id, model2.id)
self.assertListEqual(list(model.metabolites.keys()), list(model2.metabolites.keys()))
self.assertListEqual(list(model.reactions.keys()), list(model2.reactions.keys()))
self.assertListEqual(list(model.genes.keys()), list(model2.genes.keys()))
for r1, r2 in zip(list(model.reactions.values()), list(model2.reactions.values())):
self.assertEqual(r1.name, r2.name)
self.assertEqual(r1.reversible, r2.reversible)
self.assertDictEqual(r1.stoichiometry, r2.stoichiometry)
self.assertEqual(r1.lb, r2.lb)
self.assertEqual(r1.ub, r2.ub)
self.assertEqual(str(r1.gpr), str(r2.gpr))
self.assertSetEqual(set(model.genes.keys()), set(model2.genes.keys()))
def cbm_simualtion_iAF():
SBML_FILE = "../../../examples/models/Ec_iAF1260.xml"
model = load_cbmodel(SBML_FILE, flavor="cobra")
constraints = {'R_Ec_biomass_iAF1260_core_59p81M': (0.55, 9999)}
res = FBA(model, objective={"R_EX_succ_e": 1}, constraints=constraints)
for r, f in res.values.items():
if f != 0:
print(r + " --> " + str(f))
print(res.values["R_EX_succ_e"])
print(res.values["R_Ec_biomass_iAF1260_core_59p81M"])
def framed_optim(isMultiProc, size):
model = load_cbmodel(SBML_FILE, flavor="cobra")
fileRes = basePath + "Results/optim_Ec_iAF1260_ko.csv"
simulProb = StoicSimulationProblem(
model, objective={"R_Ec_biomass_iAF1260_core_59p81M": 1})
objFunc = build_evaluation_function("targetFlux",
["R_Ec_biomass_iAF1260_core_59p81M"])
cbm_strain_optim(simulProb,
evaluationFunc=objFunc,
levels=None,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def medium_SC_Etanol_Levels_optim(isMultiProc=False,
size=1,
withCobraPy=False):
SBML_FILE = "../../../examples/models/iMM904.xml"
model = load_cbmodel(SBML_FILE, flavor="fbc2")
fileRes = basePath + "Results/optim_iMM904_etanol_levels.csv"
simulProb = StoicSimulationProblem(model,
objective={"R_BIOMASS_SC5_notrace": 1},
withCobraPy=withCobraPy)
fba = simulProb.simulate()
print("Biomass: " +
str(fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"]))
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
criticalReacs = essential
# set the minimum production of biomass
simulProb.set_objective_function({"R_EX_etoh_e": 1})
simulProb.constraints.update({
"R_BIOMASS_SC5_notrace":
(fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"] * 0.75, 9999)
})
print("Biomass: " +
str(fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"] * 0.75))
evalFunc = build_evaluation_function(
"MinNumberReacAndMaxFluxWithLevels", size, LEVELS,
{"R_EX_etoh_e": model.reactions["R_EX_etoh_e"].ub})
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=LEVELS,
type=optimType.MEDIUM_LEVELS,
criticalReacs=criticalReacs,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def medium_reac_ko_optim(isMultiProc=False, size=[5, 5], withCobraPy=False):
SBML_FILE = "../../../examples/models/Ec_iAF1260.xml"
model = load_cbmodel(SBML_FILE, flavor="cobra")
newModel = fix_exchange_reactions_model(model)
fileRes = basePath + "Results/optim_Ec_iAF1260_medium_ko_succ.csv"
simulProb = StoicSimulationProblem(
newModel,
objective={"R_Ec_biomass_iAF1260_core_59p81M": 1},
withCobraPy=withCobraPy)
# set the minimum production of biomass
fba = simulProb.simulate()
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
simulProb.set_objective_function({"R_EX_succ_e_": 1})
simulProb.constraints.update({
"R_Ec_biomass_iAF1260_core_59p81M":
(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.25, 9999)
})
print(
"Biomass: " +
str(fba.get_fluxes_distribution()["R_Ec_biomass_iAF1260_core_59p81M"] *
0.25))
evalFunc = build_evaluation_function(
"MinNumberReacAndMaxFlux", sum(size),
{"R_EX_succ_e": model.reactions["R_EX_succ_e"].ub})
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM_REACTION_KO,
criticalReacs=essential,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes) # KO_Reaction by default
def cbm_simualtion():
SBML_FILE = "../../../examples/models/iMM904.xml"
model = load_cbmodel(SBML_FILE, flavor="fbc2")
constraints = {
'R_EX_so4_e': (-10000, 0),
'R_EX_o2_e': (-50, 0),
'R_EX_gam6p_e': (-10, 0),
'R_EX_melib_e': (-5, 0)
}
constraints.update({"R_BIOMASS_SC5_notrace": (0.21, 9999)})
res = FBA(model, objective={"R_EX_etoh_e": 1}, constraints=constraints)
print(res.values["R_EX_etoh_e"])
print(res.values["R_BIOMASS_SC5_notrace"])
print(res.values["R_EX_so4_e"])
print(res.values["R_EX_gam6p_e"])
print(res.values["R_EX_o2_e"])
print(res.values["R_EX_melib_e"])
def medium_SC_optim(isMultiProc=False, size=1, withCobraPy=False):
SBML_FILE = "../../../examples/models/iMM904.xml"
model = load_cbmodel(SBML_FILE, flavor="fbc2")
fileRes = basePath + "Results/optim_iMM904_medium.csv"
simulProb = StoicSimulationProblem(model,
objective={"R_BIOMASS_SC5_notrace": 1},
withCobraPy=withCobraPy)
fba = simulProb.simulate()
# calculate the essential uptake reactions to biomass production
essential = simulProb.find_essential_drains()
print(essential)
if simulProb.constraints:
simulProb.constraints.update(
{reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential}) # put essential reactions as constraints
else:
simulProb.constraints = {
reac: (StoicConfigurations.DEFAULT_LB, 0)
for reac in essential
}
criticalReacs = essential
# set the minimum production of biomass
minObjective = {
"R_BIOMASS_SC5_notrace":
fba.get_fluxes_distribution()["R_BIOMASS_SC5_notrace"] * 0.75
}
evalFunc = build_evaluation_function("MinNumberReac", size, minObjective)
cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
type=optimType.MEDIUM,
criticalReacs=criticalReacs,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes)
def reac_ko_optim(isMultiProc=False, size=1, withCobraPy=False):
SBML_FILE = "../../../examples/models/Ec_iAF1260.xml"
if withCobraPy:
model = read_sbml_model(SBML_FILE)
fileRes = basePath + "Results/optim_Ec_iAF1260_ko_cobra.csv"
else:
model = load_cbmodel(SBML_FILE, flavor="cobra")
fileRes = basePath + "Results/optim_Ec_iAF1260_ko.csv"
simulProb = StoicSimulationProblem(
model,
objective={"Ec_biomass_iAF1260_core_59p81M": 1},
withCobraPy=withCobraPy)
evalFunc = build_evaluation_function("targetFlux",
["Ec_biomass_iAF1260_core_59p81M"])
result = cbm_strain_optim(simulProb,
evaluationFunc=evalFunc,
levels=None,
isMultiProc=isMultiProc,
candidateSize=size,
resultFile=fileRes) # KO_Reaction by default
result.print()
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution = FBA(model, get_shadow_prices=True, get_reduced_costs=True)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution = FBA(model, get_shadow_prices=True, get_reduced_costs=True)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
essential = essential_genes(model)
self.assertListEqual(essential, ESSENTIAL_GENES)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution = gene_deletion(model, ROOM_GENE_KO, 'ROOM')
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertTrue(solution.values['R_EX_succ_e'] > 1e-3)
def medium_test():
SBML_FILE = "../../../examples/models/EC_SC_model.xml"
model = load_cbmodel(SBML_FILE)
model2 = fix_exchange_reactions_model(model)
for r in model2.reactions:
print(r)
for r_id, rxn in model2.reactions.items():
if r_id.startswith(
'R_EX_'): # ou tambem podes fazer if rxn.is_exchange:
rxn.lb = 0
rxn.ub = 1000 \
#if rxn.ub is None else rxn.ub
#sol 1
constraints = {
'R_EX_mn2_e__mod1': (-1000, 0),
'R_EX_cobalt2_e__mod1': (-1000, 0),
'R_EX_cl_e__mod1': (-1000, 0),
'R_EX_xyl_D_medium_': (-12, 0),
'R_EX_mobd_e__mod1': (-1000, 0),
'R_EX_mg2_e__mod1': (-1000, 0),
'R_EX_o2_medium_': (-2, 0),
'R_EX_k_medium_': (-1000, 0),
'R_EX_nh4_medium_': (-1000, 0),
'R_EX_fe3_e__mod1': (-1000, 0),
'R_EX_zn2_e__mod1': (-1000, 0),
'R_EX_ca2_e__mod1': (-1000, 0),
'R_EX_so4_medium_': (-1000, 0),
'R_EX_pi_medium_': (-1000, 0),
'R_EX_cu2_e__mod1': (-1000, 0)
}
#sol2
constraints = {
'R_EX_mn2_e__mod1': (-1000, 0),
'R_EX_cobalt2_e__mod1': (-1000, 0),
'R_EX_cl_e__mod1': (-1000, 0),
'R_EX_mobd_e__mod1': (-1000, 0),
'R_EX_cu_e__mod1': (-1000, 0),
'R_EX_mg2_e__mod1': (-1000, 0),
'R_EX_glc_D_medium_': (-12, 0),
'R_EX_o2_medium_': (-2, 0),
'R_EX_k_medium_': (-1000, 0),
'R_EX_nh4_medium_': (-1000, 0),
'R_EX_fe3_e__mod1': (-1000, 0),
'R_EX_zn2_e__mod1': (-1000, 0),
'R_EX_ca2_e__mod1': (-1000, 0),
'R_EX_so4_medium_': (-1000, 0),
'R_EX_pi_medium_': (-1000, 0)
}
res = pFBA(model2,
objective={"R_BM_total_Synth": 1},
constraints=constraints)
#res = FBA(model2, objective={"R_BM_total_Synth": 1})
for r, f in res.values.items():
print(r + " --> " + str(f))
print(res.values["R_EX_succ_medium_"])
print(res.values["R_BM_total_Synth"])
print(res.values["R_EX_glc_D_medium_"])
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
simplify(model)
solution = FBA(model)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
simplify(model)
solution = FBA(model)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
variability = FVA(model)
self.assertTrue(all([lb <= ub if lb is not None and ub is not None else True
for lb, ub in variability.values()]))
def main(mode,
noheuristics=False,
nothermo=False,
allow_unbalanced=False,
allow_blocked=False,
biomass=None,
biomass_db_path=None,
normalize_biomass=False,
taxa=None,
outputfile=None):
if mode == 'draft':
if outputfile:
universe_draft = outputfile
model_specific_data = os.path.splitext(outputfile)[0] + '.csv'
bigg_gprs = os.path.splitext(outputfile)[0] + '_gprs.csv'
else:
universe_draft = project_dir + config.get('generated',
'universe_draft')
model_specific_data = project_dir + config.get(
'generated', 'model_specific_data')
bigg_gprs = project_dir + config.get('generated', 'bigg_gprs')
build_bigg_universe_model(universe_draft)
data = download_model_specific_data(model_specific_data)
create_gpr_table(data, outputfile=bigg_gprs)
elif mode == 'thermo':
universe_draft = project_dir + config.get('generated',
'universe_draft')
equilibrator_compounds = project_dir + config.get(
'input', 'equilibrator_compounds')
if outputfile:
bigg_gibbs = outputfile
else:
bigg_gibbs = project_dir + config.get('generated', 'bigg_gibbs')
compute_bigg_gibbs_energy(universe_draft, equilibrator_compounds,
bigg_gibbs)
elif mode == 'curated':
universe_draft = project_dir + config.get('generated',
'universe_draft')
model_specific_data = project_dir + config.get('generated',
'model_specific_data')
if not biomass:
if taxa == 'archaea':
biomass = 'archaea'
else:
biomass = config.get('universe', 'default_biomass')
if outputfile:
universe_final = outputfile
else:
tag = taxa if taxa != 'bacteria' else biomass
universe_final = "{}{}universe_{}.xml.gz".format(
project_dir, config.get('generated', 'folder'), tag)
bigg_models = project_dir + config.get('input', 'bigg_models')
bigg_models = pd.read_csv(bigg_models, index_col=0)
manual_curation = project_dir + config.get('input', 'manually_curated')
manually_curated = pd.read_csv(manual_curation, index_col=0)
unbalanced = project_dir + config.get('input',
'unbalanced_metabolites')
unbalanced = pd.read_csv(unbalanced, header=None)
unbalanced = unbalanced[0].tolist()
try:
model = load_cbmodel(universe_draft,
flavor=config.get('sbml', 'default_flavor'))
model_specific_data = pd.read_csv(model_specific_data)
except IOError:
raise IOError(
'Universe draft not found. Please run --draft first to download BiGG data.'
)
if biomass_db_path is None:
biomass_db_path = project_dir + config.get('input',
'biomass_library')
biomass_db = load_biomass_db(biomass_db_path,
normalize_weight=normalize_biomass,
model=model)
if biomass not in biomass_db:
valid_ids = ','.join(biomass_db.keys())
raise RuntimeError(
'Biomass identifier not in database. Currently in database: ' +
valid_ids)
biomass_eq = biomass_db[biomass]
if nothermo:
thermodynamics_data = None
metabolomics_data = None
else:
try:
bigg_gibbs = project_dir + config.get('generated',
'bigg_gibbs')
thermodynamics_data = pd.read_csv(bigg_gibbs, index_col=0)
except IOError:
raise IOError(
'Thermodynamic data not found. Please run --thermo first to generate thermodynamic data.'
)
metabolomics = project_dir + config.get('input', 'metabolomics')
metabolomics_data = pd.read_csv(metabolomics, index_col=1)
curate_universe(model,
taxa=taxa,
outputfile=universe_final,
model_specific_data=model_specific_data,
bigg_models=bigg_models,
thermodynamics_data=thermodynamics_data,
metabolomics_data=metabolomics_data,
manually_curated=manually_curated,
unbalanced_metabolites=unbalanced,
biomass_eq=biomass_eq,
use_heuristics=(not noheuristics),
remove_unbalanced=(not allow_unbalanced),
remove_blocked=(not allow_blocked))
else:
print('Unrecognized option:', mode)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution = gene_deletion(model, DOUBLE_GENE_KO, 'pFBA')
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.values[model.biomass_reaction], DOUBLE_KO_GROWTH_RATE, 2)
self.assertAlmostEqual(solution.values['R_EX_succ_e'], DOUBLE_KO_SUCC_EX, 2)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
solution = gene_deletion(model, ROOM_GENE_KO, 'ROOM')
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertTrue(solution.values['R_EX_succ_e'] > 1e-3)
def testRun(self):
model = load_cbmodel(SMALL_TEST_MODEL, flavor='cobra')
essential = essential_genes(model)
self.assertListEqual(essential, ESSENTIAL_GENES)
