Source code for cobra.medium.minimal_medium

"""Provide functions and helpers to obtain minimal growth media."""

import logging
from typing import TYPE_CHECKING, Iterable, Union

import pandas as pd
from optlang.interface import OPTIMAL
from optlang.symbolics import Zero

from .boundary_types import find_boundary_types

    from cobra import Model, Reaction

[docs]logger = logging.getLogger(__name__)
[docs]def add_linear_obj(model: "Model") -> None: r"""Add a linear version of a minimal medium to the model solver. Changes the optimization objective to finding the growth medium requiring the smallest total import flux: ..math:: minimize \sum_{r_i in import_reactions} |r_i| Parameters ---------- model : cobra.Model The cobra model to modify. """ coefs = {} for rxn in find_boundary_types(model, "exchange"): export = len(rxn.reactants) == 1 if export: coefs[rxn.reverse_variable] = 1 else: coefs[rxn.forward_variable] = 1 model.objective.set_linear_coefficients(coefs) model.objective.direction = "min"
[docs]def add_mip_obj(model: "Model") -> None: """Add a mixed-integer version of a minimal medium to the model. Changes the optimization objective to finding the medium with the least components: minimize size(R) where R part of import_reactions Arguments --------- model : cobra.model The model to modify. """ if len(model.variables) > 1e4: logger.warning( "The MIP version of minimal media is extremely slow for " "models that large :(" ) exchange_rxns = find_boundary_types(model, "exchange") big_m = max(abs(b) for r in exchange_rxns for b in r.bounds) prob = model.problem coefs = {} to_add = [] for rxn in exchange_rxns: export = len(rxn.reactants) == 1 indicator = prob.Variable("ind_" +, lb=0, ub=1, type="binary") if export: vrv = rxn.reverse_variable indicator_const = prob.Constraint( vrv - indicator * big_m, ub=0, name="ind_constraint_" + ) else: vfw = rxn.forward_variable indicator_const = prob.Constraint( vfw - indicator * big_m, ub=0, name="ind_constraint_" + ) to_add.extend([indicator, indicator_const]) coefs[indicator] = 1 model.add_cons_vars(to_add) model.solver.update() model.objective.set_linear_coefficients(coefs) model.objective.direction = "min"
[docs]def _as_medium( exchanges: Iterable["Reaction"], tolerance: float = 1e-6, exports: bool = False ) -> pd.Series: """Convert a solution to medium. Parameters ---------- exchanges : list of cobra.reaction The exchange reactions to consider. tolerance : float > 0, optional The absolute tolerance for fluxes. Fluxes with an absolute value smaller than this number will be ignored (default 1e-6). exports : bool, optional Whether to return export fluxes as well (default False). Returns ------- pandas.Series The "medium", meaning all active import fluxes in the solution. """ logger.debug("Formatting medium.") medium = pd.Series() for rxn in exchanges: export = len(rxn.reactants) == 1 flux = rxn.flux if abs(flux) < tolerance: continue if export: medium[] = -flux elif not export: medium[] = flux if not exports: medium = medium[medium > 0] return medium
[docs]def minimal_medium( model: "Model", min_objective_value: float = 0.1, exports: bool = False, minimize_components: Union[bool, int] = False, open_exchanges: bool = False, ) -> Union[pd.Series, pd.DataFrame, None]: """Find the minimal growth medium for the `model`. Finds the minimal growth medium for the `model` which allows for model as well as individual growth. Here, a minimal medium can either be the medium requiring the smallest total import flux or the medium requiring the least components (ergo ingredients), which will be much slower due to being a mixed integer problem (MIP). Parameters ---------- model : cobra.model The model to modify. min_objective_value : float > 0 or array-like object, optional The minimum growth rate (objective) that has to be achieved (default 0.1). exports : bool, optional Whether to include export fluxes in the returned medium. Defaults to False which will only return import fluxes (default False). minimize_components : bool or int > 0, optional Whether to minimize the number of components instead of the total import flux. Might be more intuitive if set to True, but may also be slow to calculate for large communities. If set to a number `n` will return up to `n` alternative solutions all with the same number of components (default False). open_exchanges : bool or number, optional Whether to ignore currently set bounds and make all exchange reactions in the `model` possible. If set to a `number`, all exchange reactions will be opened with (-`number`, `number`) as bounds (default False). Returns ------- pandas.Series, pandas.DataFrame or None A pandas.Series giving the import flux for each required import reaction and (optionally) the associated export fluxes. All exchange fluxes are oriented into the import reaction e.g. positive fluxes denote imports and negative fluxes exports. If `minimize_components` is a number larger than 1, may return a pandas.DataFrame where each column is a minimal medium. Returns None, if the minimization is infeasible (for instance if min_growth > maximum growth rate). Notes ----- Due to numerical issues, the `minimize_components` option will usually only minimize the number of "large" import fluxes. Specifically, the detection limit is given by ``integrality_tolerance * max_bound`` where ``max_bound`` is the largest bound on an import reaction. Thus, if you are interested in small import fluxes as well you may have to adjust the solver tolerance at first with `model.tolerance = 1e-7` for instance. However, this will be *very* slow for large models especially with GLPK. """ exchange_rxns = find_boundary_types(model, "exchange") if isinstance(open_exchanges, bool): open_bound = 1000 else: open_bound = open_exchanges with model as mod: if open_exchanges: logger.debug(f"Opening exchanges for {len(exchange_rxns)} imports.") for rxn in exchange_rxns: rxn.bounds = (-open_bound, open_bound) logger.debug("Applying objective value constraints.") obj_const = mod.problem.Constraint( mod.objective.expression, lb=min_objective_value, name="medium_obj_constraint", ) mod.add_cons_vars([obj_const]) mod.solver.update() mod.objective = Zero logger.debug("Adding new media objective.") tol = mod.solver.configuration.tolerances.feasibility if minimize_components: add_mip_obj(mod) if isinstance(minimize_components, bool): minimize_components = 1 seen = set() best = num_components = mod.slim_optimize() if mod.solver.status != OPTIMAL: logger.warning("Minimization of medium was infeasible.") return None exclusion = mod.problem.Constraint(Zero, ub=0) mod.add_cons_vars([exclusion]) mod.solver.update() media = [] for i in range(minimize_components):"Finding alternative medium #{(i + 1)}.") vars = [mod.variables["ind_" + s] for s in seen] if len(seen) > 0: exclusion.set_linear_coefficients(dict.fromkeys(vars, 1)) exclusion.ub = best - 1 num_components = mod.slim_optimize() if mod.solver.status != OPTIMAL or num_components > best: if i == 0: logger.warning( "Could not get an optimal solution. " "This is usually due to numerical instability. " "Possible remedies are relaoding the model " "from scratch, switching to a different solver, " "or decreasing the solver tolerance. Please, " "carefully read the note on numerical instability " "in the function documentation." ) return None break medium = _as_medium(exchange_rxns, tol, exports=exports) media.append(medium) seen.update(medium[medium > 0].index) if len(media) > 1: medium = pd.concat(media, axis=1, sort=True).fillna(0.0) medium.sort_index(axis=1, inplace=True) else: medium = media[0] else: add_linear_obj(mod) mod.slim_optimize() if mod.solver.status != OPTIMAL: logger.warning("Minimization of medium was infeasible.") return None medium = _as_medium(exchange_rxns, tol, exports=exports) return medium