def _add_switches(self, reactions):
logger.info("Adding switches.")
y_vars = list()
switches = list()
self._exchanges = list()
for reaction in reactions:
if reaction.id.startswith('DM_'):
# demand reactions don't need integer switches
self._exchanges.append(reaction)
continue
y = self.model.solver.interface.Variable('y_' + reaction.id, lb=0, ub=1, type='binary')
y_vars.append(y)
# The following is a complicated but efficient way to write the following constraints
# switch_lb = self.model.solver.interface.Constraint(y * reaction.lower_bound - reaction.flux_expression,
# name='switch_lb_' + reaction.id, ub=0)
# switch_ub = self.model.solver.interface.Constraint(y * reaction.upper_bound - reaction.flux_expression,
# name='switch_ub_' + reaction.id, lb=0)
forward_var_term = Mul._from_args((RealNumber(-1), reaction.forward_variable))
reverse_var_term = Mul._from_args((RealNumber(-1), reaction.reverse_variable))
switch_lb_y_term = Mul._from_args((RealNumber(reaction.lower_bound), y))
switch_ub_y_term = Mul._from_args((RealNumber(reaction.upper_bound), y))
switch_lb = self.model.solver.interface.Constraint(
Add._from_args((switch_lb_y_term, forward_var_term, reverse_var_term)), name='switch_lb_' + reaction.id,
ub=0, sloppy=True)
switch_ub = self.model.solver.interface.Constraint(
Add._from_args((switch_ub_y_term, forward_var_term, reverse_var_term)), name='switch_ub_' + reaction.id,
lb=0, sloppy=True)
switches.extend([switch_lb, switch_ub])
self.model.solver.add(y_vars)
self.model.solver.add(switches, sloppy=True)
logger.info("Setting minimization of switch variables as objective.")
self.model.objective = self.model.solver.interface.Objective(Add(*y_vars), direction='min')
self._y_vars_ids = [var.name for var in y_vars]
def _add_reaction_dual_constraint(self, reaction, coefficient, maximization, prefix):
"""Add a dual constraint corresponding to the reaction's objective coefficient"""
stoichiometry = {self.solver.variables["lambda_" + m.id]: c for m, c in six.iteritems(reaction.metabolites)}
if maximization:
constraint = self.solver.interface.Constraint(
Add._from_args(tuple(c * v for v, c in six.iteritems(stoichiometry))),
name="r_%s_%s" % (reaction.id, prefix),
lb=coefficient)
else:
constraint = self._dual_solver.interface.Constraint(
Add._from_args(tuple(c * v for v, c in six.iteritems(stoichiometry))),
name="r_%s_%s" % (reaction.id, prefix),
ub=coefficient)
self.solver._add_constraint(constraint)
def _add_reaction_dual_constraint(self, reaction, coefficient, maximization, prefix):
"""Add a dual constraint corresponding to the reaction's objective coefficient"""
stoichiometry = {self.solver.variables["lambda_" + m.id]: c for m, c in six.iteritems(reaction.metabolites)}
if maximization:
constraint = self.solver.interface.Constraint(
Add._from_args(tuple(c * v for v, c in six.iteritems(stoichiometry))),
name="r_%s_%s" % (reaction.id, prefix),
lb=coefficient)
else:
constraint = self._dual_solver.interface.Constraint(
Add._from_args(tuple(c * v for v, c in six.iteritems(stoichiometry))),
name="r_%s_%s" % (reaction.id, prefix),
ub=coefficient)
self.solver._add_constraint(constraint)
def _add_switches(self, reactions):
logger.info("Adding switches.")
y_vars = list()
switches = list()
self._exchanges = list()
for reaction in reactions:
if reaction.id.startswith('DM_'):
# demand reactions don't need integer switches
self._exchanges.append(reaction)
continue
y = self.model.solver.interface.Variable('y_' + reaction.id,
lb=0,
ub=1,
type='binary')
y_vars.append(y)
# The following is a complicated but efficient way to write the following constraints
# switch_lb = self.model.solver.interface.Constraint(y * reaction.lower_bound - reaction.flux_expression,
# name='switch_lb_' + reaction.id, ub=0)
# switch_ub = self.model.solver.interface.Constraint(y * reaction.upper_bound - reaction.flux_expression,
# name='switch_ub_' + reaction.id, lb=0)
forward_var_term = Mul._from_args(
(RealNumber(-1), reaction.forward_variable))
reverse_var_term = Mul._from_args(
(RealNumber(-1), reaction.reverse_variable))
switch_lb_y_term = Mul._from_args(
(RealNumber(reaction.lower_bound), y))
switch_ub_y_term = Mul._from_args(
(RealNumber(reaction.upper_bound), y))
switch_lb = self.model.solver.interface.Constraint(
Add._from_args(
(switch_lb_y_term, forward_var_term, reverse_var_term)),
name='switch_lb_' + reaction.id,
ub=0,
sloppy=True)
switch_ub = self.model.solver.interface.Constraint(
Add._from_args(
(switch_ub_y_term, forward_var_term, reverse_var_term)),
name='switch_ub_' + reaction.id,
lb=0,
sloppy=True)
switches.extend([switch_lb, switch_ub])
self.model.solver.add(y_vars)
self.model.solver.add(switches, sloppy=True)
logger.info("Setting minimization of switch variables as objective.")
self.model.objective = self.model.solver.interface.Objective(
Add(*y_vars), direction='min')
self._y_vars_ids = [var.name for var in y_vars]
def remove_infeasible_cycles(model, fluxes, fix=()):
"""Remove thermodynamically infeasible cycles from a flux distribution.
Parameters
---------
model : cobra.Model
The model that generated the flux distribution.
fluxes : dict
The flux distribution containing infeasible loops.
Returns
-------
dict
A cycle free flux distribution.
References
----------
.. [1] A. A. Desouki, F. Jarre, G. Gelius-Dietrich, and M. J. Lercher, “CycleFreeFlux: efficient removal of
thermodynamically infeasible loops from flux distributions.”
"""
with model:
# make sure the original object is restored
exchange_reactions = model.boundary
exchange_ids = [exchange.id for exchange in exchange_reactions]
internal_reactions = [reaction for reaction in model.reactions if reaction.id not in exchange_ids]
for exchange in exchange_reactions:
exchange_flux = fluxes[exchange.id]
exchange.bounds = (exchange_flux, exchange_flux)
cycle_free_objective_list = []
for internal_reaction in internal_reactions:
internal_flux = fluxes[internal_reaction.id]
if internal_flux >= 0:
cycle_free_objective_list.append(Mul._from_args((FloatOne, internal_reaction.forward_variable)))
internal_reaction.bounds = (0, internal_flux)
else: # internal_flux < 0:
cycle_free_objective_list.append(Mul._from_args((FloatOne, internal_reaction.reverse_variable)))
internal_reaction.bounds = (internal_flux, 0)
cycle_free_objective = model.solver.interface.Objective(
Add._from_args(cycle_free_objective_list), direction="min", sloppy=True
)
model.objective = cycle_free_objective
for reaction_id in fix:
reaction_to_fix = model.reactions.get_by_id(reaction_id)
reaction_to_fix.bounds = (fluxes[reaction_id], fluxes[reaction_id])
try:
solution = model.optimize(raise_error=True)
except OptimizationError as e:
logger.warning("Couldn't remove cycles from reference flux distribution.")
raise e
result = solution.fluxes
return result
def remove_infeasible_cycles(model, fluxes, fix=()):
"""Remove thermodynamically infeasible cycles from a flux distribution.
Arguments
---------
model : cobra.Model
The model that generated the flux distribution.
fluxes : dict
The flux distribution containing infeasible loops.
Returns
-------
dict
A cycle free flux distribution.
References
----------
.. [1] A. A. Desouki, F. Jarre, G. Gelius-Dietrich, and M. J. Lercher, “CycleFreeFlux: efficient removal of
thermodynamically infeasible loops from flux distributions.”
"""
with model:
# make sure the original object is restored
exchange_reactions = model.boundary
exchange_ids = [exchange.id for exchange in exchange_reactions]
internal_reactions = [reaction for reaction in model.reactions if reaction.id not in exchange_ids]
for exchange in exchange_reactions:
exchange_flux = fluxes[exchange.id]
exchange.bounds = (exchange_flux, exchange_flux)
cycle_free_objective_list = []
for internal_reaction in internal_reactions:
internal_flux = fluxes[internal_reaction.id]
if internal_flux >= 0:
cycle_free_objective_list.append(Mul._from_args((FloatOne, internal_reaction.forward_variable)))
internal_reaction.bounds = (0, internal_flux)
else: # internal_flux < 0:
cycle_free_objective_list.append(Mul._from_args((FloatOne, internal_reaction.reverse_variable)))
internal_reaction.bounds = (internal_flux, 0)
cycle_free_objective = model.solver.interface.Objective(
Add._from_args(cycle_free_objective_list), direction="min", sloppy=True
)
model.objective = cycle_free_objective
for reaction_id in fix:
reaction_to_fix = model.reactions.get_by_id(reaction_id)
reaction_to_fix.bounds = (fluxes[reaction_id], fluxes[reaction_id])
try:
solution = model.optimize(raise_error=True)
except OptimizationError as e:
logger.warning("Couldn't remove cycles from reference flux distribution.")
raise e
result = solution.fluxes
return result
def remove_infeasible_cycles(model, fluxes, fix=()):
"""Remove thermodynamically infeasible cycles from a flux distribution.
Arguments
---------
model : SolverBasedModel
The model that generated the flux distribution.
fluxes : dict
The flux distribution containing infeasible loops.
Returns
-------
dict
A cycle free flux distribution.
References
----------
.. [1] A. A. Desouki, F. Jarre, G. Gelius-Dietrich, and M. J. Lercher, “CycleFreeFlux: efficient removal of
thermodynamically infeasible loops from flux distributions.”
"""
with TimeMachine() as tm:
# make sure the original object is restored
tm(do=int, undo=partial(setattr, model, 'objective', model.objective))
exchange_reactions = model.exchanges
exchange_ids = [exchange.id for exchange in exchange_reactions]
internal_reactions = [
reaction for reaction in model.reactions
if reaction.id not in exchange_ids
]
for exchange in exchange_reactions:
exchange_flux = fluxes[exchange.id]
tm(do=partial(setattr, exchange, 'lower_bound', exchange_flux),
undo=partial(setattr, exchange, 'lower_bound',
exchange.lower_bound))
tm(do=partial(setattr, exchange, 'upper_bound', exchange_flux),
undo=partial(setattr, exchange, 'upper_bound',
exchange.upper_bound))
cycle_free_objective_list = []
for internal_reaction in internal_reactions:
internal_flux = fluxes[internal_reaction.id]
if internal_flux >= 0:
cycle_free_objective_list.append(
Mul._from_args(
(FloatOne, internal_reaction.forward_variable)))
tm(do=partial(setattr, internal_reaction, 'lower_bound', 0),
undo=partial(setattr, internal_reaction, 'lower_bound',
internal_reaction.lower_bound))
tm(do=partial(setattr, internal_reaction, 'upper_bound',
internal_flux),
undo=partial(setattr, internal_reaction, 'upper_bound',
internal_reaction.upper_bound))
else: # internal_flux < 0:
cycle_free_objective_list.append(
Mul._from_args(
(FloatOne, internal_reaction.reverse_variable)))
tm(do=partial(setattr, internal_reaction, 'lower_bound',
internal_flux),
undo=partial(setattr, internal_reaction, 'lower_bound',
internal_reaction.lower_bound))
tm(do=partial(setattr, internal_reaction, 'upper_bound', 0),
undo=partial(setattr, internal_reaction, 'upper_bound',
internal_reaction.upper_bound))
cycle_free_objective = model.solver.interface.Objective(
Add._from_args(cycle_free_objective_list),
direction="min",
sloppy=True)
model.objective = cycle_free_objective
for reaction_id in fix:
reaction_to_fix = model.reactions.get_by_id(reaction_id)
tm(do=partial(setattr, reaction_to_fix, 'lower_bound',
fluxes[reaction_id]),
undo=partial(setattr, reaction_to_fix, 'lower_bound',
reaction_to_fix.lower_bound))
tm(do=partial(setattr, reaction_to_fix, 'upper_bound',
fluxes[reaction_id]),
undo=partial(setattr, reaction_to_fix, 'upper_bound',
reaction_to_fix.upper_bound))
try:
solution = model.solve()
except SolveError as e:
logger.warning(
"Couldn't remove cycles from reference flux distribution.")
raise e
result = solution.x_dict
return result
def remove_infeasible_cycles(model, fluxes, fix=()):
"""Remove thermodynamically infeasible cycles from a flux distribution.
Arguments
---------
model : SolverBasedModel
The model that generated the flux distribution.
fluxes : dict
The flux distribution containing infeasible loops.
Returns
-------
dict
A cycle free flux distribution.
References
----------
.. [1] A. A. Desouki, F. Jarre, G. Gelius-Dietrich, and M. J. Lercher, “CycleFreeFlux: efficient removal of
thermodynamically infeasible loops from flux distributions.”
"""
with TimeMachine() as tm:
# make sure the original object is restored
tm(do=int, undo=partial(setattr, model, 'objective', model.objective))
exchange_reactions = model.exchanges
exchange_ids = [exchange.id for exchange in exchange_reactions]
internal_reactions = [reaction for reaction in model.reactions if reaction.id not in exchange_ids]
for exchange in exchange_reactions:
exchange_flux = fluxes[exchange.id]
tm(do=partial(setattr, exchange, 'lower_bound', exchange_flux),
undo=partial(setattr, exchange, 'lower_bound', exchange.lower_bound))
tm(do=partial(setattr, exchange, 'upper_bound', exchange_flux),
undo=partial(setattr, exchange, 'upper_bound', exchange.upper_bound))
cycle_free_objective_list = []
for internal_reaction in internal_reactions:
internal_flux = fluxes[internal_reaction.id]
if internal_flux >= 0:
cycle_free_objective_list.append(Mul._from_args((FloatOne, internal_reaction.forward_variable)))
tm(do=partial(setattr, internal_reaction, 'lower_bound', 0),
undo=partial(setattr, internal_reaction, 'lower_bound', internal_reaction.lower_bound))
tm(do=partial(setattr, internal_reaction, 'upper_bound', internal_flux),
undo=partial(setattr, internal_reaction, 'upper_bound', internal_reaction.upper_bound))
else: # internal_flux < 0:
cycle_free_objective_list.append(Mul._from_args((FloatOne, internal_reaction.reverse_variable)))
tm(do=partial(setattr, internal_reaction, 'lower_bound', internal_flux),
undo=partial(setattr, internal_reaction, 'lower_bound', internal_reaction.lower_bound))
tm(do=partial(setattr, internal_reaction, 'upper_bound', 0),
undo=partial(setattr, internal_reaction, 'upper_bound', internal_reaction.upper_bound))
cycle_free_objective = model.solver.interface.Objective(
Add._from_args(cycle_free_objective_list), direction="min", sloppy=True
)
model.objective = cycle_free_objective
for reaction_id in fix:
reaction_to_fix = model.reactions.get_by_id(reaction_id)
tm(do=partial(setattr, reaction_to_fix, 'lower_bound', fluxes[reaction_id]),
undo=partial(setattr, reaction_to_fix, 'lower_bound', reaction_to_fix.lower_bound))
tm(do=partial(setattr, reaction_to_fix, 'upper_bound', fluxes[reaction_id]),
undo=partial(setattr, reaction_to_fix, 'upper_bound', reaction_to_fix.upper_bound))
try:
solution = model.solve()
except SolveError as e:
logger.warning("Couldn't remove cycles from reference flux distribution.")
raise e
result = solution.x_dict
return result