def find_unconserved_metabolites(model):
"""
Detect unconserved metabolites.
Parameters
----------
model : cobra.Model
The metabolic model under investigation.
Notes
-----
See [1]_ section 3.2 for a complete description of the algorithm.
.. [1] Gevorgyan, A., M. G Poolman, and D. A Fell.
"Detection of Stoichiometric Inconsistencies in Biomolecular
Models."
Bioinformatics 24, no. 19 (2008): 2245.
"""
problem = model.problem
stoich_trans = problem.Model()
internal_rxns = con_helpers.get_internals(model)
metabolites = set(met for rxn in internal_rxns for met in rxn.metabolites)
# The binary variables k[i] in the paper.
k_vars = list()
for met in metabolites:
# The element m[i] of the mass vector.
m_var = problem.Variable(met.id)
k_var = problem.Variable("k_{}".format(met.id), type="binary")
k_vars.append(k_var)
stoich_trans.add([m_var, k_var])
# This constraint is equivalent to 0 <= k[i] <= m[i].
stoich_trans.add(
problem.Constraint(k_var - m_var,
ub=0,
name="switch_{}".format(met.id)))
stoich_trans.update()
con_helpers.add_reaction_constraints(stoich_trans, internal_rxns,
problem.Constraint)
# The objective is to maximize the binary indicators k[i], subject to the
# above inequality constraints.
stoich_trans.objective = problem.Objective(Zero,
sloppy=True,
direction="max")
stoich_trans.objective.set_linear_coefficients({var: 1. for var in k_vars})
status = stoich_trans.optimize()
if status == OPTIMAL:
# TODO: See if that could be a Boolean test `bool(var.primal)`.
return set([
model.metabolites.get_by_id(var.name[2:]) for var in k_vars
if var.primal < 0.8
])
else:
raise RuntimeError(
"Could not compute list of unconserved metabolites."
" Solver status is '{}' (only optimal expected).".format(status))
def check_stoichiometric_consistency(model):
"""
Verify the consistency of the model's stoichiometry.
Parameters
----------
model : cobra.Model
The metabolic model under investigation.
Notes
-----
See [1]_ section 3.1 for a complete description of the algorithm.
.. [1] Gevorgyan, A., M. G Poolman, and D. A Fell.
"Detection of Stoichiometric Inconsistencies in Biomolecular
Models."
Bioinformatics 24, no. 19 (2008): 2245.
"""
problem = model.problem
# The transpose of the stoichiometric matrix N.T in the paper.
stoich_trans = problem.Model()
# We clone the existing configuration in order to apply non-default
# settings, for example, for solver verbosity or timeout.
stoich_trans.configuration = problem.Configuration.clone(
config=model.solver.configuration, problem=stoich_trans)
internal_rxns = con_helpers.get_internals(model)
metabolites = set(met for rxn in internal_rxns for met in rxn.metabolites)
LOGGER.info("model '%s' has %d internal reactions", model.id,
len(internal_rxns))
LOGGER.info("model '%s' has %d internal metabolites", model.id,
len(metabolites))
stoich_trans.add([problem.Variable(m.id, lb=1) for m in metabolites])
stoich_trans.update()
con_helpers.add_reaction_constraints(stoich_trans, internal_rxns,
problem.Constraint)
# The objective is to minimize the metabolite mass vector.
stoich_trans.objective = problem.Objective(Zero,
direction="min",
sloppy=True)
stoich_trans.objective.set_linear_coefficients(
{var: 1.
for var in stoich_trans.variables})
status = stoich_trans.optimize()
if status == OPTIMAL:
return True
elif status == INFEASIBLE:
return False
else:
raise RuntimeError(
"Could not determine stoichiometric consistencty."
" Solver status is '{}'"
" (only optimal or infeasible expected).".format(status))
def check_stoichiometric_consistency(model):
"""
Verify the consistency of the model stoichiometry.
Parameters
----------
model : cobra.Model
The metabolic model under investigation.
Notes
-----
See [1]_ section 3.1 for a complete description of the algorithm.
.. [1] Gevorgyan, A., M. G Poolman, and D. A Fell.
"Detection of Stoichiometric Inconsistencies in Biomolecular Models."
Bioinformatics 24, no. 19 (2008): 2245.
"""
Model, Constraint, Variable, Objective = con_helpers.get_interface(model)
# The transpose of the stoichiometric matrix N.T in the paper.
stoich_trans = Model()
internal_rxns = con_helpers.get_internals(model)
metabolites = set(met for rxn in internal_rxns for met in rxn.metabolites)
LOGGER.info("model '%s' has %d internal reactions", model.id,
len(internal_rxns))
LOGGER.info("model '%s' has %d internal metabolites", model.id,
len(metabolites))
for metabolite in metabolites:
stoich_trans.add(Variable(metabolite.id, lb=1))
stoich_trans.update()
con_helpers.add_reaction_constraints(stoich_trans, internal_rxns,
Constraint)
# The objective is to minimize the metabolite mass vector.
stoich_trans.objective = Objective(1)
stoich_trans.objective.set_linear_coefficients(
{var: 1.
for var in stoich_trans.variables})
stoich_trans.objective.direction = "min"
status = stoich_trans.optimize()
if status == "optimal":
return True
elif status == "infeasible":
return False
else:
raise RuntimeError(
"Could not determine stoichiometric consistencty."
" Solver status is '{}'"
" (only optimal or infeasible expected).".format(status))