Source code for cobra.flux_analysis.fastcc
"""Provide an implementation of FASTCC."""
from typing import TYPE_CHECKING, List, Optional
from optlang.symbolics import Zero
from .helpers import normalize_cutoff
if TYPE_CHECKING:
from cobra.core import Model, Reaction
[docs]def _find_sparse_mode(
model: "Model", rxns: List["Reaction"], flux_threshold: float, zero_cutoff: float
) -> List["Reaction"]:
"""Perform the LP required for FASTCC.
Parameters
----------
model: cobra.Model
The model to perform FASTCC on.
rxns: list of cobra.Reaction
The reactions to use for LP.
flux_threshold: float
The upper threshold an auxiliary variable can have.
zero_cutoff: float
The cutoff below which flux is considered zero.
Returns
-------
list of cobra.Reaction
The list of reactions to consider as consistent.
"""
if rxns:
obj_vars = []
vars_and_cons = []
prob = model.problem
for rxn in rxns:
var = prob.Variable(
"auxiliary_{}".format(rxn.id), lb=0.0, ub=flux_threshold
)
const = prob.Constraint(
rxn.forward_variable + rxn.reverse_variable - var,
name="constraint_{}".format(rxn.id),
lb=0.0,
)
vars_and_cons.extend([var, const])
obj_vars.append(var)
model.add_cons_vars(vars_and_cons)
model.objective = prob.Objective(Zero, sloppy=True)
model.objective.set_linear_coefficients({v: 1.0 for v in obj_vars})
model.optimize(objective_sense="max")
result = [rxn for rxn in model.reactions if abs(rxn.flux) > zero_cutoff]
else:
result = []
return result
[docs]def _flip_coefficients(model: "Model", rxns: List["Reaction"]) -> None:
"""Flip the coefficients for optimizing in reverse direction.
Parameters
----------
model: cobra.Model
The model to operate on.
rxns: list of cobra.Reaction
The list of reactions whose coefficients will be flipped.
"""
# flip reactions
for rxn in rxns:
const = model.constraints.get("constraint_{}".format(rxn.id))
var = model.variables.get("auxiliary_{}".format(rxn.id))
coefs = const.get_linear_coefficients(const.variables)
const.set_linear_coefficients({k: -v for k, v in coefs.items() if k is not var})
# flip objective
objective = model.objective
objective_coefs = objective.get_linear_coefficients(objective.variables)
objective.set_linear_coefficients({k: -v for k, v in objective_coefs.items()})
[docs]def fastcc(
model: "Model", flux_threshold: float = 1.0, zero_cutoff: Optional[float] = None
) -> "Model":
r"""
Check consistency of a metabolic network using FASTCC [1]_.
FASTCC (Fast Consistency Check) is an algorithm for rapid and
efficient consistency check in metabolic networks. FASTCC is
a pure LP implementation and is low on computation resource
demand. FASTCC also circumvents the problem associated with
reversible reactions for the purpose. Given a global model,
it will generate a consistent global model i.e., remove
blocked reactions. For more details on FASTCC, please
check [1]_.
Parameters
----------
model: cobra.Model
The model to operate on.
flux_threshold: float, optional
The flux threshold to consider (default 1.0).
zero_cutoff: float, optional
The cutoff to consider for zero flux (default model.tolerance).
Returns
-------
cobra.Model
The consistent model.
Notes
-----
The LP used for FASTCC is like so:
maximize: \sum_{i \in J} z_i
s.t. : z_i \in [0, \varepsilon] \forall i \in J, z_i \in \mathbb{R}_+
v_i \ge z_i \forall i \in J
Sv = 0 v \in B
References
----------
.. [1] Vlassis N, Pacheco MP, Sauter T (2014)
Fast Reconstruction of Compact Context-Specific Metabolic Network
Models.
PLoS Comput Biol 10(1): e1003424. doi:10.1371/journal.pcbi.1003424
"""
zero_cutoff = normalize_cutoff(model, zero_cutoff)
irreversible_rxns = [rxn for rxn in model.reactions if not rxn.reversibility]
rxns_to_check = irreversible_rxns
with model:
rxns_to_keep = _find_sparse_mode(
model, rxns_to_check, flux_threshold, zero_cutoff
)
rxns_to_check = list(set(model.reactions).difference(rxns_to_keep))
while rxns_to_check:
with model:
new_rxns = _find_sparse_mode(
model, rxns_to_check, flux_threshold, zero_cutoff
)
rxns_to_keep.extend(new_rxns)
# this condition will be valid for all but the last iteration
if list(set(rxns_to_check).intersection(rxns_to_keep)):
rxns_to_check = list(set(rxns_to_check).difference(rxns_to_keep))
else:
rxns_to_flip = list(set(rxns_to_check).difference(irreversible_rxns))
_flip_coefficients(model, rxns_to_flip)
sol = model.optimize(min)
to_add_rxns = sol.fluxes.index[sol.fluxes.abs() > zero_cutoff].tolist()
rxns_to_keep.extend(
[model.reactions.get_by_id(rxn) for rxn in to_add_rxns]
)
# since this is the last iteration, it needs to break or else
# it will run forever since rxns_to_check won't be empty
break
consistent_rxns = set(rxns_to_keep)
# need the ids since Reaction objects are created fresh with model.copy()
rxns_to_remove = [
rxn.id for rxn in set(model.reactions).difference(consistent_rxns)
]
consistent_model = model.copy()
consistent_model.remove_reactions(rxns_to_remove, remove_orphans=True)
return consistent_model