def gpr_eflux(model, gene_exp, scale_rxn, scale_value, constraints=None, parsimonious=False):
max_exp = max(gene_exp.values())
bounds = {}
for r_id, (lb, ub) in model.bounds.items():
if r_id.startswith('u_'):
gene = r_id[len('u_'):]
val = gene_exp[gene] / max_exp if gene in gene_exp else 1
bounds[r_id] = (0, val)
else:
ub2 = 1 if ub is None or ub > 0 else 0
bounds[r_id] = (0, ub2)
if constraints:
constraints = model.convert_constraints(constraints)
for r_id, x in constraints.items():
ub = x[1] if isinstance(x, tuple) else x
ub2 = 1 if ub is None or ub > 0 else 0
bounds[r_id] = (0, ub2)
if parsimonious:
sol = pFBA(model, constraints=bounds)
else:
sol = FBA(model, constraints=bounds)
sol.values_converted = model.convert_fluxes(sol.values)
k = abs(scale_value / sol.values_converted[scale_rxn])
for r_id, val in sol.values_converted.items():
sol.values_converted[r_id] = val * k
return sol
def gpr_GIMME(model,
gene_exp,
cutoff=25,
growth_frac=0.9,
constraints=None,
parsimonious=False):
threshold = percentile(gene_exp.values(), cutoff)
coeffs = {
'u_' + gene: threshold - val
for gene, val in gene_exp.items() if val < threshold
}
if constraints:
constraints = model.convert_constraints(constraints)
else:
constraints = {}
pre_solution = FBA(model, constraints=constraints)
biomass = model.detect_biomass_reaction()
constraints[biomass] = (growth_frac * pre_solution.values[biomass], None)
if parsimonious:
solution = pFBA(model,
objective=coeffs,
minimize=True,
constraints=constraints)
else:
solution = FBA(model,
objective=coeffs,
minimize=True,
constraints=constraints)
solution.values_converted = model.convert_fluxes(solution.values)
return solution
def gene_pFBA(model, objective=None, minimize=False, constraints=None, solver=None):
reactions = [r_id for r_id in model.reactions if r_id.startswith('u_')]
if constraints:
constraints = model.convert_constraints(constraints)
sol = pFBA(model, objective, minimize, constraints, reactions, solver)
sol.values_converted = model.convert_fluxes(sol.values)
return sol
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
solution1 = pFBA(model)
solution2 = FBA(model)
self.assertEqual(solution1.status, Status.OPTIMAL)
self.assertEqual(solution2.status, Status.OPTIMAL)
growth1 = solution1.values[model.detect_biomass_reaction()]
growth2 = solution2.values[model.detect_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 gpr_GIMME(model, gene_exp, cutoff=25, growth_frac=0.9, constraints=None, parsimonious=False):
threshold = percentile(gene_exp.values(), cutoff)
coeffs = {'u_' + gene: threshold-val for gene, val in gene_exp.items() if val < threshold}
if constraints:
constraints = model.convert_constraints(constraints)
else:
constraints = {}
pre_solution = FBA(model, constraints=constraints)
biomass = model.detect_biomass_reaction()
constraints[biomass] = (growth_frac * pre_solution.values[biomass], None)
if parsimonious:
solution = pFBA(model, objective=coeffs, minimize=True, constraints=constraints)
else:
solution = FBA(model, objective=coeffs, minimize=True, constraints=constraints)
solution.values_converted = model.convert_fluxes(solution.values)
return solution
def gpr_eflux(model,
gene_exp,
scale_rxn,
scale_value,
constraints=None,
parsimonious=False):
max_exp = max(gene_exp.values())
bounds = {}
for r_id, (lb, ub) in model.bounds.items():
if r_id.startswith('u_'):
gene = r_id[len('u_'):]
val = gene_exp[gene] / max_exp if gene in gene_exp else 1
bounds[r_id] = (0, val)
else:
ub2 = 1 if ub is None or ub > 0 else 0
bounds[r_id] = (0, ub2)
if constraints:
constraints = model.convert_constraints(constraints)
for r_id, x in constraints.items():
ub = x[1] if isinstance(x, tuple) else x
ub2 = 1 if ub is None or ub > 0 else 0
bounds[r_id] = (0, ub2)
if parsimonious:
sol = pFBA(model, constraints=bounds)
else:
sol = FBA(model, constraints=bounds)
sol.values_converted = model.convert_fluxes(sol.values)
k = abs(scale_value / sol.values_converted[scale_rxn])
for r_id, val in sol.values_converted.items():
sol.values_converted[r_id] = val * k
return sol
def arFBA(model, reference_constraints, perturbed_constraints, weights):
""" Run an allosteric regulated FBA (arFBA) simulation
arguments:
- model: an instance of AllostericModel (generated by the SBML loader)
- reference_constraints: dict of reaction id to (lower bound, upper bound)
- perturbed_constraints: dict of reaction id to (lower bound, upper bound)
- weights: dict of (metabolite id, reaction id) to float (weighting factor for each allosteric interaction)
returns:
- flux distribution: dict of reaction id to float
"""
TOL = 1e-6
M = 1e3
solution_ref = pFBA(model, constraints=reference_constraints)
v0 = solution_ref.values
t0 = compute_turnover(model, v0)
model_irrev, mapping = build_perturbed_model(model, perturbed_constraints)
solver = solver_instance()
solver.build_problem(model_irrev)
m_r_lookup = model_irrev.metabolite_reaction_lookup_table()
reg_m_r_lookup = model_irrev.metabolite_reaction_regulatory_lookup_table()
regulators = [m_id for m_id, targets in reg_m_r_lookup.items() if len(targets) > 0]
for m_id in regulators:
solver.add_variable('t_' + m_id, 0, None, persistent=False, update_problem=False)
for fwd_id, bwd_id in mapping.values():
solver.add_variable('y_' + fwd_id, vartype=VarType.BINARY, persistent=False, update_problem=False)
solver.add_variable('y_' + bwd_id, vartype=VarType.BINARY, persistent=False, update_problem=False)
for (m_id, r_id), kind in model.regulation.items():
if v0[r_id] > TOL and t0[m_id] > TOL:
diff_pos = 'd+_{}_{}'.format(m_id, r_id)
diff_neg = 'd-_{}_{}'.format(m_id, r_id)
solver.add_variable(diff_pos, 0, None, persistent=False, update_problem=False)
solver.add_variable(diff_neg, 0, None, persistent=False, update_problem=False)
solver.update()
for m_id in regulators:
lhs = {r_id: coeff for r_id, coeff in m_r_lookup[m_id].items() if coeff > 0}
lhs['t_' + m_id] = -1
solver.add_constraint('ct_' + m_id, lhs.items(), persistent=False, update_problem=False)
for r_id, (fwd_id, bwd_id) in mapping.items():
solver.add_constraint('c_' + fwd_id, {fwd_id: 1, 'y_' + fwd_id: -M}.items(), '<', 0, persistent=False, update_problem=False)
solver.add_constraint('c_' + bwd_id, {bwd_id: 1, 'y_' + bwd_id: -M}.items(), '<', 0, persistent=False, update_problem=False)
solver.add_constraint('rev_' + r_id, {'y_' + fwd_id: 1, 'y_' + bwd_id: 1}.items(), '<', 1, persistent=False, update_problem=False)
for (m_id, r_id), kind in model.regulation.items():
if v0[r_id] > TOL and t0[m_id] > TOL:
diff_pos = 'd+_{}_{}'.format(m_id, r_id)
diff_neg = 'd-_{}_{}'.format(m_id, r_id)
if r_id in mapping:
fwd_id, bwd_id = mapping[r_id]
if kind == 1:
lhs = {diff_pos: 1, fwd_id: -1/v0[r_id], bwd_id: -1/v0[r_id], 't_' + m_id: 1/t0[m_id]}
solver.add_constraint('c' + diff_pos, lhs.items(), '>', 0, persistent=False, update_problem=False)
lhs = {diff_neg: 1, fwd_id: 1/v0[r_id], bwd_id: 1/v0[r_id], 't_' + m_id: -1/t0[m_id]}
solver.add_constraint('c' + diff_neg, lhs.items(), '>', 0, persistent=False, update_problem=False)
else:
lhs = {diff_pos: 1, fwd_id: -1/v0[r_id], bwd_id: -1/v0[r_id], 't_' + m_id: -1/t0[m_id]}
solver.add_constraint('c' + diff_pos, lhs.items(), '>', -2, persistent=False, update_problem=False)
lhs = {diff_neg: 1, fwd_id: 1/v0[r_id], bwd_id: 1/v0[r_id], 't_' + m_id: 1/t0[m_id]}
solver.add_constraint('c' + diff_neg, lhs.items(), '>', 2, persistent=False, update_problem=False)
else:
if kind == 1:
lhs = {diff_pos: 1, r_id: -1/v0[r_id], 't_' + m_id: 1/t0[m_id]}
solver.add_constraint('c' + diff_pos, lhs.items(), '>', 0, persistent=False, update_problem=False)
lhs = {diff_neg: 1, r_id: 1/v0[r_id], 't_' + m_id: -1/t0[m_id]}
solver.add_constraint('c' + diff_neg, lhs.items(), '>', 0, persistent=False, update_problem=False)
else:
lhs = {diff_pos: 1, r_id: -1/v0[r_id], 't_' + m_id: -1/t0[m_id]}
solver.add_constraint('c' + diff_pos, lhs.items(), '>', -2, persistent=False, update_problem=False)
lhs = {diff_neg: 1, r_id: 1/v0[r_id], 't_' + m_id: 1/t0[m_id]}
solver.add_constraint('c' + diff_neg, lhs.items(), '>', 2, persistent=False, update_problem=False)
solver.update()
objective = {r_id: -1 for r_id in model_irrev.reactions}
for (m_id, r_id) in model.regulation.keys():
if (m_id, r_id) in weights and v0[r_id] > TOL and t0[m_id] > TOL:
diff_pos = 'd+_{}_{}'.format(m_id, r_id)
diff_neg = 'd-_{}_{}'.format(m_id, r_id)
objective[diff_pos] = -weights[(m_id, r_id)]
objective[diff_neg] = -weights[(m_id, r_id)]
solution = solver.solve_lp(objective)
if solution.status == Status.OPTIMAL:
v = merge_fluxes(model, mapping, solution.values)
else:
v = None
return v
