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 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')
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 find_essential_drains(self):
drains = self.get_drains()
if self.constraints:
constraints = {
d: (StoicConfigurations.DEFAULT_LB,
StoicConfigurations.DEFAULT_UB)
for d in drains if d not in self.constraints.keys()
}
else:
constraints = {
d: (StoicConfigurations.DEFAULT_LB,
StoicConfigurations.DEFAULT_UB)
for d in drains
}
solution = FBA(self.model,
objective=self.objective,
minimize=self.minimize,
constraints=constraints)
# #which objetive reactions has flux whem drains are all open
hasFlux = []
for rId in self.objective.keys():
if solution.values[rId] > 0:
hasFlux.append(rId)
# close each drain and check if the objective reactions have flux
essential = []
for d in drains:
constraints[d] = (0, StoicConfigurations.DEFAULT_UB)
solution = FBA(self.model,
objective=self.objective,
minimize=self.minimize,
constraints=constraints)
if (solution.values is None
or any([solution.values[rId] == 0 for rId in hasFlux])):
essential.append(d)
constraints[d] = (StoicConfigurations.DEFAULT_LB,
StoicConfigurations.DEFAULT_UB)
return essential
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 _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 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 GIMME(model, gene_exp, cutoff=25, growth_frac=0.9, constraints=None, parsimonious=False):
""" Run a GIMME simulation (Becker and Palsson, 2008).
Arguments:
model (CBModel): model
gene_exp (dict): transcriptomics data
cutoff (int): percentile cuttof (default: 25)
growth_frac (float): minimum growth requirement (default: 0.9)
constraints (dict): additional constraints
parsimonious (bool): compute a parsimonious solution (default: False)
Returns:
Solution: solution
"""
rxn_exp = gene_to_reaction_expression(model, gene_exp, or_func=max)
threshold = percentile(rxn_exp.values(), cutoff)
coeffs = {r_id: threshold-val for r_id, val in rxn_exp.items() if val < threshold}
solver = solver_instance(model)
wt_solution = FBA(model, constraints=constraints, solver=solver)
if not constraints:
constraints = {}
biomass = model.biomass_reaction
constraints[biomass] = (growth_frac * wt_solution.values[biomass], None)
for r_id in model.reactions:
if model.reactions[r_id].reversible:
pos, neg = r_id + '+', r_id + '-'
solver.add_variable(pos, 0, None, persistent=False, update_problem=False)
solver.add_variable(neg, 0, None, persistent=False, update_problem=False)
solver.update()
for r_id in model.reactions:
if model.reactions[r_id].reversible:
pos, neg = r_id + '+', r_id + '-'
solver.add_constraint('c' + pos, {r_id: -1, pos: 1}, '>', 0, persistent=False, update_problem=False)
solver.add_constraint('c' + neg, {r_id: 1, neg: 1}, '>', 0, persistent=False, update_problem=False)
solver.update()
objective = dict()
for r_id, val in coeffs.items():
if model.reactions[r_id].reversible:
pos, neg = r_id + '+', r_id + '-'
objective[pos] = val
objective[neg] = val
else:
objective[r_id] = val
solution = solver.solve(objective, minimize=True, constraints=constraints)
if parsimonious:
pre_solution = solution
solver.add_constraint('obj', objective, '=', pre_solution.fobj)
objective = dict()
for r_id in model.reactions:
if model.reactions[r_id].reversible:
pos, neg = r_id + '+', r_id + '-'
objective[pos] = 1
objective[neg] = 1
else:
objective[r_id] = 1
solution = solver.solve(objective, minimize=True, constraints=constraints)
solver.remove_constraint('obj')
solution.pre_solution = pre_solution
return solution
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')
simplify(model)
solution = FBA(model)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)