def lp_sampler(model, n_samples=1000, weights=None, constraints=None, select_probability=0.01,
futile_cycle_threshold=1e2, variation_threshold=1e-4, merge_keys=False, verbose=True):
if not weights:
variability = FVA(model, constraints=constraints)
weights = {r_id: 1.0 / (ub - lb) for r_id, (lb, ub) in variability.items()
if ub is not None and lb is not None
and variation_threshold < (ub - lb) < futile_cycle_threshold}
samples = []
solver = solver_instance(model)
for i in range(n_samples):
objective = {r_id: gauss(0, 1)*W for r_id, W in weights.items()
if random() < select_probability}
sol = pFBA(model, objective=objective, constraints=constraints, solver=solver)
if sol.status == Status.OPTIMAL:
samples.append(sol.values)
if verbose:
print('Sampling success rate: {} (of {})'.format(len(samples), n_samples))
if merge_keys:
merged = OrderedDict()
for r_id in model.reactions:
merged[r_id] = [sample[r_id] for sample in samples]
samples = merged
return samples
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 _run_stoic_simutation(model, objective, minimize, constraints, method):
if method == 'FBA':
solution = FBA(model,
objective=objective,
minimize=minimize,
constraints=constraints)
elif method == 'pFBA':
solution = pFBA(model,
objective=objective,
minimize=minimize,
constraints=constraints)
else:
raise Exception("Unknown method to perform the simulation.")
return solution.status, solution.values
def eFlux(model, gene_exp, scale_rxn=None, scale_value=None, constraints=None, parsimonious=False):
""" Run an E-Flux simulation (Colijn et al, 2009).
Arguments:
model (CBModel): model
gene_exp (dict): transcriptomics data
scale_rxn (str): reaction to scale flux vector (optional)
scale_value (float): scaling factor (mandatory if scale_rxn is specified)
constraints (dict): additional constraints (optional)
parsimonious (bool): compute a parsimonious solution (default: False)
Returns:
Solution: solution
"""
rxn_exp = gene_to_reaction_expression(model, gene_exp)
max_exp = max(rxn_exp.values())
bounds = {}
for r_id, reaction in model.reactions.items():
val = rxn_exp[r_id] / max_exp if r_id in rxn_exp else 1
lb2 = -val if reaction.lb is None or reaction.lb < 0 else 0
ub2 = val if reaction.ub is None or reaction.ub > 0 else 0
bounds[r_id] = (lb2, ub2)
if constraints:
for r_id, x in constraints.items():
lb, ub = x if isinstance(x, tuple) else (x, x)
lb2 = -1 if lb is None or lb < 0 else 0
ub2 = 1 if ub is None or ub > 0 else 0
bounds[r_id] = (lb2, ub2)
if parsimonious:
sol = pFBA(model, constraints=bounds)
else:
sol = FBA(model, constraints=bounds)
if scale_rxn is not None:
if sol.values[scale_rxn] != 0:
k = abs(scale_value / sol.values[scale_rxn])
else:
k = 0
for r_id, val in sol.values.items():
sol.values[r_id] = val * k
return sol
def lp_sampler(model,
n_samples=1000,
weights=None,
constraints=None,
select_probability=0.01,
futile_cycle_threshold=1e2,
variation_threshold=1e-4,
merge_keys=False,
verbose=True):
if not weights:
variability = FVA(model, constraints=constraints)
weights = {
r_id: 1.0 / (ub - lb)
for r_id, (lb, ub) in variability.items()
if ub is not None and lb is not None and variation_threshold <
(ub - lb) < futile_cycle_threshold
}
samples = []
solver = solver_instance(model)
for i in range(n_samples):
objective = {
r_id: gauss(0, 1) * W
for r_id, W in weights.items() if random() < select_probability
}
sol = pFBA(model,
objective=objective,
constraints=constraints,
solver=solver)
if sol.status == Status.OPTIMAL:
samples.append(sol.values)
if verbose:
print('Sampling success rate: {} (of {})'.format(
len(samples), n_samples))
if merge_keys:
merged = OrderedDict()
for r_id in model.reactions:
merged[r_id] = [sample[r_id] for sample in samples]
samples = merged
return samples
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_"])