Source code for cobra.flux_analysis.parsimonious

# -*- coding: utf-8 -*-

from __future__ import absolute_import

import logging
from itertools import chain
from warnings import warn

from optlang.symbolics import Zero

from cobra.core.solution import get_solution
from cobra.util import solver as sutil

[docs]LOGGER = logging.getLogger(__name__)
[docs]def optimize_minimal_flux(*args, **kwargs): warn("optimize_minimal_flux has been renamed to pfba", DeprecationWarning) return pfba(*args, **kwargs)
[docs]def pfba(model, fraction_of_optimum=1.0, objective=None, reactions=None): """Perform basic pFBA (parsimonious Enzyme Usage Flux Balance Analysis) to minimize total flux. 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 fraction_of_optimum : float, optional Fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than maximal_value * fraction_of_optimum. objective : dict or model.problem.Objective 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. reactions : iterable List of reactions or reaction identifiers. Implies `return_frame` to be true. Only return fluxes for the given reactions. Faster than fetching all fluxes if only a few are needed. 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, objective=None, fraction_of_optimum=1.0): """Add pFBA objective Add objective to minimize the summed flux of all reactions to the current objective. See Also ------- pfba Parameters ---------- model : cobra.Model The model to add the objective to objective : An objective to set in combination with the pFBA objective. fraction_of_optimum : float Fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than maximal_value * fraction_of_optimum. """ if objective is not None: model.objective = objective if == "_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})