# Source code for cobra.flux_analysis.parsimonious

```
"""Provide parsimonious FBA implementation."""
from itertools import chain
from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
from warnings import warn
from optlang.symbolics import Zero
from ..core.solution import get_solution
from ..util import solver as sutil
if TYPE_CHECKING:
from optlang.interface import Objective
from cobra import Model, Reaction, Solution
[docs]def optimize_minimal_flux(
*args, **kwargs
) -> Callable[["Model", float, Union[Dict, "Objective"], List["Reaction"]], "Solution"]:
"""Perform basic pFBA to minimize total flux.
.. deprecated:: 0.6.0a4
`optimize_minimal_flux` will be removed in cobrapy 1.0.0, it is
replaced by `pfba`.
Parameters
----------
*args: Any
Non-keyword variable-length arguments.
**kwargs: Any
Keyword-only variable-length arguments.
Returns
-------
A function performing the parsimonious FBA.
"""
warn("optimize_minimal_flux has been renamed to pfba", DeprecationWarning)
return pfba(*args, **kwargs)
[docs]def pfba(
model: "Model",
fraction_of_optimum: float = 1.0,
objective: Union[Dict, "Objective", None] = None,
reactions: Optional[List["Reaction"]] = None,
) -> "Solution":
"""Perform basic pFBA (parsimonious Enzyme Usage Flux Balance Analysis).
pFBA [1] adds the minimization of all fluxes the the objective of the
model. This approach is motivated by the idea that high fluxes have a
higher enzyme turn-over and that since producing enzymes is costly,
the cell will try to minimize overall flux while still maximizing the
original objective function, e.g. the growth rate.
Parameters
----------
model : cobra.Model
The model to perform pFBA on.
fraction_of_optimum : float, optional
The fraction of optimum which must be maintained. The original
objective reaction is constrained to be greater than maximal value
times the `fraction_of_optimum` (default 1.0).
objective : dict or cobra.Model.objective, optional
A desired objective to use during optimization in addition to the
pFBA objective. Dictionaries (reaction as key, coefficient as value)
can be used for linear objectives (default None).
reactions : list of cobra.Reaction, optional
List of cobra.Reaction. Implies `return_frame` to be true. Only
return fluxes for the given reactions. Faster than fetching all
fluxes if only a few are needed (default None).
Returns
-------
cobra.Solution
The solution object to the optimized model with pFBA constraints
added.
References
----------
.. [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A.,
Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data
from evolved E. coli are consistent with computed optimal growth from
genome-scale models. Molecular Systems Biology, 6,
390. doi:10.1038/msb.2010.47
"""
reactions = (
model.reactions if reactions is None else model.reactions.get_by_any(reactions)
)
with model as m:
add_pfba(m, objective=objective, fraction_of_optimum=fraction_of_optimum)
m.slim_optimize(error_value=None)
solution = get_solution(m, reactions=reactions)
return solution
[docs]def add_pfba(
model: "Model",
objective: Union[Dict, "Objective", None] = None,
fraction_of_optimum: float = 1.0,
) -> None:
"""Add pFBA objective to the `model`.
This adds objective to minimize the summed flux of all reactions to the
current objective.
Parameters
----------
model : cobra.Model
The model to add the objective to.
objective : dict or cobra.Model.objective, optional
A desired objective to use during optimization in addition to the
pFBA objective. Dictionaries (reaction as key, coefficient as value)
can be used for linear objectives (default None).
fraction_of_optimum : float, optional
Fraction of optimum which must be maintained. The original objective
reaction is constrained to be greater than maximal value times the
`fraction_of_optimum`.
See Also
-------
pfba
"""
if objective is not None:
model.objective = objective
if model.solver.objective.name == "_pfba_objective":
raise ValueError("The model already has a pFBA objective.")
sutil.fix_objective_as_constraint(model, fraction=fraction_of_optimum)
reaction_variables = (
(rxn.forward_variable, rxn.reverse_variable) for rxn in model.reactions
)
variables = chain(*reaction_variables)
model.objective = model.problem.Objective(
Zero, direction="min", sloppy=True, name="_pfba_objective"
)
model.objective.set_linear_coefficients({v: 1.0 for v in variables})
```