from ..core import Reaction, Metabolite
from ..manipulation.modify import convert_to_irreversible
from six import iteritems
[docs]def construct_loopless_model(cobra_model):
"""construct a loopless model
This adds MILP constraints to prevent flux from proceeding in a loop, as
done in http://dx.doi.org/10.1016/j.bpj.2010.12.3707
Please see the documentation for an explanation of the algorithm.
This must be solved with an MILP capable solver.
"""
# copy the model and make it irreversible
model = cobra_model.copy()
convert_to_irreversible(model)
max_ub = max(model.reactions.list_attr("upper_bound"))
# a dict for storing S^T
thermo_stoic = {"thermo_var_" + metabolite.id: {}
for metabolite in model.metabolites}
# Slice operator is so that we don't get newly added metabolites
original_metabolites = model.metabolites[:]
for reaction in model.reactions[:]:
# Boundary reactions are not subjected to these constraints
if len(reaction._metabolites) == 1:
continue
# populate the S^T dict
bound_id = "thermo_bound_" + reaction.id
for met, stoic in iteritems(reaction._metabolites):
thermo_stoic["thermo_var_" + met.id][bound_id] = stoic
# I * 1000 > v --> I * 1000 - v > 0
reaction_ind = Reaction(reaction.id + "_indicator")
reaction_ind.variable_kind = "integer"
reaction_ind.upper_bound = 1
reaction_ub = Metabolite(reaction.id + "_ind_ub")
reaction_ub._constraint_sense = "G"
reaction.add_metabolites({reaction_ub: -1})
reaction_ind.add_metabolites({reaction_ub: max_ub})
# This adds a compensating term for 0 flux reactions, so we get
# S^T x - (1 - I) * 1001 < -1 which becomes
# S^T x < 1000 for 0 flux reactions and
# S^T x < -1 for reactions with nonzero flux.
reaction_bound = Metabolite(bound_id)
reaction_bound._constraint_sense = "L"
reaction_bound._bound = max_ub
reaction_ind.add_metabolites({reaction_bound: max_ub + 1})
model.add_reaction(reaction_ind)
for metabolite in original_metabolites:
metabolite_var = Reaction("thermo_var_" + metabolite.id)
metabolite_var.lower_bound = -max_ub
model.add_reaction(metabolite_var)
metabolite_var.add_metabolites(
{model.metabolites.get_by_id(k): v
for k, v in iteritems(thermo_stoic[metabolite_var.id])})
return model