Source code for cobra.core.model

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

"""Define the Model class."""

from __future__ import absolute_import

import logging
import types
from copy import copy, deepcopy
from functools import partial
from warnings import warn

import optlang
import six
from optlang.symbolics import Basic, Zero
from six import iteritems, string_types

from cobra.core.configuration import Configuration
from cobra.core.dictlist import DictList
from cobra.core.gene import Gene
from cobra.core.group import Group
from cobra.core.metabolite import Metabolite
from cobra.core.object import Object
from cobra.core.reaction import Reaction
from cobra.core.solution import get_solution
from cobra.exceptions import SolverNotFound
from cobra.medium import find_boundary_types, find_external_compartment, sbo_terms
from cobra.util.context import HistoryManager, get_context, resettable
from cobra.util.solver import (
    add_cons_vars_to_problem,
    assert_optimal,
    interface_to_str,
    remove_cons_vars_from_problem,
    set_objective,
    solvers,
)
from cobra.util.util import AutoVivification, format_long_string


[docs]logger = logging.getLogger(__name__)
[docs]configuration = Configuration()
[docs]class Model(Object): """Class representation for a cobra model Parameters ---------- id_or_model : Model, string Either an existing Model object in which case a new model object is instantiated with the same properties as the original model, or an identifier to associate with the model as a string. name : string Human readable name for the model Attributes ---------- reactions : DictList A DictList where the key is the reaction identifier and the value a Reaction metabolites : DictList A DictList where the key is the metabolite identifier and the value a Metabolite genes : DictList A DictList where the key is the gene identifier and the value a Gene groups : DictList A DictList where the key is the group identifier and the value a Group solution : Solution The last obtained solution from optimizing the model. """
[docs] def __setstate__(self, state): """Make sure all cobra.Objects in the model point to the model.""" self.__dict__.update(state) for y in ["reactions", "genes", "metabolites"]: for x in getattr(self, y): x._model = self if not hasattr(self, "name"): self.name = None
[docs] def __getstate__(self): """Get state for serialization. Ensures that the context stack is cleared prior to serialization, since partial functions cannot be pickled reliably. """ odict = self.__dict__.copy() odict["_contexts"] = [] return odict
def __init__(self, id_or_model=None, name=None): if isinstance(id_or_model, Model): Object.__init__(self, name=name) self.__setstate__(id_or_model.__dict__) if not hasattr(self, "name"): self.name = None self._solver = id_or_model.solver else: Object.__init__(self, id_or_model, name=name) self._trimmed = False self._trimmed_genes = [] self._trimmed_reactions = {} self.genes = DictList() self.reactions = DictList() # A list of cobra.Reactions self.metabolites = DictList() # A list of cobra.Metabolites self.groups = DictList() # A list of cobra.Groups # genes based on their ids {Gene.id: Gene} self._compartments = {} self._contexts = [] # from cameo ... # if not hasattr(self, '_solver'): # backwards compatibility # with older cobrapy pickles? interface = configuration.solver self._solver = interface.Model() self._solver.objective = interface.Objective(Zero) self._populate_solver(self.reactions, self.metabolites) self._tolerance = None self.tolerance = configuration.tolerance @property
[docs] def solver(self): """Get or set the attached solver instance. The associated the solver object, which manages the interaction with the associated solver, e.g. glpk. This property is useful for accessing the optimization problem directly and to define additional non-metabolic constraints. Examples -------- >>> import cobra.test >>> model = cobra.test.create_test_model("textbook") >>> new = model.problem.Constraint(model.objective.expression, >>> lb=0.99) >>> model.solver.add(new) """ return self._solver
@solver.setter @resettable def solver(self, value): not_valid_interface = SolverNotFound( "%s is not a valid solver interface. Pick from %s." % (value, list(solvers)) ) if isinstance(value, six.string_types): try: interface = solvers[interface_to_str(value)] except KeyError: raise not_valid_interface elif isinstance(value, types.ModuleType) and hasattr(value, "Model"): interface = value elif isinstance(value, optlang.interface.Model): interface = value.interface else: raise not_valid_interface # Do nothing if the solver did not change if self.problem == interface: return self._solver = interface.Model.clone(self._solver) @property
[docs] def tolerance(self): return self._tolerance
@tolerance.setter def tolerance(self, value): solver_tolerances = self._solver.configuration.tolerances try: solver_tolerances.feasibility = value except AttributeError: logger.info( "The current solver doesn't allow setting" "feasibility tolerance." ) try: solver_tolerances.optimality = value except AttributeError: logger.info( "The current solver doesn't allow setting" "optimality tolerance." ) try: solver_tolerances.integrality = value except AttributeError: logger.info( "The current solver doesn't allow setting" "integrality tolerance." ) self._tolerance = value @property
[docs] def description(self): warn("description deprecated", DeprecationWarning) return self.name if self.name is not None else ""
@description.setter def description(self, value): self.name = value warn("description deprecated", DeprecationWarning)
[docs] def get_metabolite_compartments(self): """Return all metabolites' compartments.""" warn("use Model.compartments instead", DeprecationWarning) return { met.compartment for met in self.metabolites if met.compartment is not None
} @property
[docs] def compartments(self): return { met.compartment: self._compartments.get(met.compartment, "") for met in self.metabolites if met.compartment is not None
} @compartments.setter def compartments(self, value): """Get or set the dictionary of current compartment descriptions. Assigning a dictionary to this property updates the model's dictionary of compartment descriptions with the new values. Parameters ---------- value : dict Dictionary mapping compartments abbreviations to full names. Examples -------- >>> import cobra.test >>> model = cobra.test.create_test_model("textbook") >>> model.compartments = {'c': 'the cytosol'} {'c': 'the cytosol', 'e': 'extracellular'} """ self._compartments.update(value) @property
[docs] def medium(self): def is_active(reaction): """Determine if a boundary reaction permits flux towards creating metabolites """ return (bool(reaction.products) and (reaction.upper_bound > 0)) or ( bool(reaction.reactants) and (reaction.lower_bound < 0) ) def get_active_bound(reaction): """For an active boundary reaction, return the relevant bound""" if reaction.reactants: return -reaction.lower_bound elif reaction.products: return reaction.upper_bound return { rxn.id: get_active_bound(rxn) for rxn in self.exchanges if is_active(rxn)
} @medium.setter def medium(self, medium): """Get or set the constraints on the model exchanges. `model.medium` returns a dictionary of the bounds for each of the boundary reactions, in the form of `{rxn_id: bound}`, where `bound` specifies the absolute value of the bound in direction of metabolite creation (i.e., lower_bound for `met <--`, upper_bound for `met -->`) Parameters ---------- medium: dictionary-like The medium to initialize. medium should be a dictionary defining `{rxn_id: bound}` pairs. """ def set_active_bound(reaction, bound): if reaction.reactants: reaction.lower_bound = -bound elif reaction.products: reaction.upper_bound = bound # Set the given media bounds media_rxns = list() exchange_rxns = frozenset(self.exchanges) for rxn_id, bound in iteritems(medium): rxn = self.reactions.get_by_id(rxn_id) if rxn not in exchange_rxns: logger.warn( "%s does not seem to be an" " an exchange reaction. Applying bounds anyway.", rxn.id, ) media_rxns.append(rxn) set_active_bound(rxn, bound) media_rxns = frozenset(media_rxns) # Turn off reactions not present in media for rxn in exchange_rxns - media_rxns: set_active_bound(rxn, 0)
[docs] def __add__(self, other_model): """Add the content of another model to this model (+). The model is copied as a new object, with a new model identifier, and copies of all the reactions in the other model are added to this model. The objective is the sum of the objective expressions for the two models. """ warn("use model.merge instead", DeprecationWarning) return self.merge(other_model, objective="sum", inplace=False)
[docs] def __iadd__(self, other_model): """Incrementally add the content of another model to this model (+=). Copies of all the reactions in the other model are added to this model. The objective is the sum of the objective expressions for the two models. """ warn("use model.merge instead", DeprecationWarning) return self.merge(other_model, objective="sum", inplace=True)
[docs] def copy(self): """Provides a partial 'deepcopy' of the Model. All of the Metabolite, Gene, and Reaction objects are created anew but in a faster fashion than deepcopy """ new = self.__class__() do_not_copy_by_ref = { "metabolites", "reactions", "genes", "notes", "annotation", "groups", } for attr in self.__dict__: if attr not in do_not_copy_by_ref: new.__dict__[attr] = self.__dict__[attr] new.notes = deepcopy(self.notes) new.annotation = deepcopy(self.annotation) new.metabolites = DictList() do_not_copy_by_ref = {"_reaction", "_model"} for metabolite in self.metabolites: new_met = metabolite.__class__() for attr, value in iteritems(metabolite.__dict__): if attr not in do_not_copy_by_ref: new_met.__dict__[attr] = copy(value) if attr == "formula" else value new_met._model = new new.metabolites.append(new_met) new.genes = DictList() for gene in self.genes: new_gene = gene.__class__(None) for attr, value in iteritems(gene.__dict__): if attr not in do_not_copy_by_ref: new_gene.__dict__[attr] = ( copy(value) if attr == "formula" else value ) new_gene._model = new new.genes.append(new_gene) new.reactions = DictList() do_not_copy_by_ref = {"_model", "_metabolites", "_genes"} for reaction in self.reactions: new_reaction = reaction.__class__() for attr, value in iteritems(reaction.__dict__): if attr not in do_not_copy_by_ref: new_reaction.__dict__[attr] = copy(value) new_reaction._model = new new.reactions.append(new_reaction) # update awareness for metabolite, stoic in iteritems(reaction._metabolites): new_met = new.metabolites.get_by_id(metabolite.id) new_reaction._metabolites[new_met] = stoic new_met._reaction.add(new_reaction) for gene in reaction._genes: new_gene = new.genes.get_by_id(gene.id) new_reaction._genes.add(new_gene) new_gene._reaction.add(new_reaction) new.groups = DictList() do_not_copy_by_ref = {"_model", "_members"} # Groups can be members of other groups. We initialize them first and # then update their members. for group in self.groups: new_group = group.__class__(group.id) for attr, value in iteritems(group.__dict__): if attr not in do_not_copy_by_ref: new_group.__dict__[attr] = copy(value) new_group._model = new new.groups.append(new_group) for group in self.groups: new_group = new.groups.get_by_id(group.id) # update awareness, as in the reaction copies new_objects = [] for member in group.members: if isinstance(member, Metabolite): new_object = new.metabolites.get_by_id(member.id) elif isinstance(member, Reaction): new_object = new.reactions.get_by_id(member.id) elif isinstance(member, Gene): new_object = new.genes.get_by_id(member.id) elif isinstance(member, Group): new_object = new.genes.get_by_id(member.id) else: raise TypeError( "The group member {!r} is unexpectedly not a " "metabolite, reaction, gene, nor another " "group.".format(member) ) new_objects.append(new_object) new_group.add_members(new_objects) try: new._solver = deepcopy(self.solver) # Cplex has an issue with deep copies except Exception: # pragma: no cover new._solver = copy(self.solver) # pragma: no cover # it doesn't make sense to retain the context of a copied model so # assign a new empty context new._contexts = list() return new
[docs] def add_metabolites(self, metabolite_list): """Will add a list of metabolites to the model object and add new constraints accordingly. The change is reverted upon exit when using the model as a context. Parameters ---------- metabolite_list : A list of `cobra.core.Metabolite` objects """ if not hasattr(metabolite_list, "__iter__"): metabolite_list = [metabolite_list] if len(metabolite_list) == 0: return None # First check whether the metabolites exist in the model metabolite_list = [x for x in metabolite_list if x.id not in self.metabolites] bad_ids = [ m for m in metabolite_list if not isinstance(m.id, string_types) or len(m.id) < 1 ] if len(bad_ids) != 0: raise ValueError("invalid identifiers in {}".format(repr(bad_ids))) for x in metabolite_list: x._model = self self.metabolites += metabolite_list # from cameo ... to_add = [] for met in metabolite_list: if met.id not in self.constraints: constraint = self.problem.Constraint(Zero, name=met.id, lb=0, ub=0) to_add += [constraint] self.add_cons_vars(to_add) context = get_context(self) if context: context(partial(self.metabolites.__isub__, metabolite_list)) for x in metabolite_list: # Do we care? context(partial(setattr, x, "_model", None))
[docs] def remove_metabolites(self, metabolite_list, destructive=False): """Remove a list of metabolites from the the object. The change is reverted upon exit when using the model as a context. Parameters ---------- metabolite_list : list A list with `cobra.Metabolite` objects as elements. destructive : bool If False then the metabolite is removed from all associated reactions. If True then all associated reactions are removed from the Model. """ if not hasattr(metabolite_list, "__iter__"): metabolite_list = [metabolite_list] # Make sure metabolites exist in model metabolite_list = [x for x in metabolite_list if x.id in self.metabolites] for x in metabolite_list: x._model = None # remove reference to the metabolite in all groups associated_groups = self.get_associated_groups(x) for group in associated_groups: group.remove_members(x) if not destructive: for the_reaction in list(x._reaction): the_coefficient = the_reaction._metabolites[x] the_reaction.subtract_metabolites({x: the_coefficient}) else: for x in list(x._reaction): x.remove_from_model() self.metabolites -= metabolite_list to_remove = [self.solver.constraints[m.id] for m in metabolite_list] self.remove_cons_vars(to_remove) context = get_context(self) if context: context(partial(self.metabolites.__iadd__, metabolite_list)) for x in metabolite_list: context(partial(setattr, x, "_model", self))
[docs] def add_reaction(self, reaction): """Will add a cobra.Reaction object to the model, if reaction.id is not in self.reactions. Parameters ---------- reaction : cobra.Reaction The reaction to add Deprecated (0.6). Use `~cobra.Model.add_reactions` instead """ warn("add_reaction deprecated. Use add_reactions instead", DeprecationWarning) self.add_reactions([reaction])
[docs] def add_boundary( self, metabolite, type="exchange", reaction_id=None, lb=None, ub=None, sbo_term=None, ): """ Add a boundary reaction for a given metabolite. There are three different types of pre-defined boundary reactions: exchange, demand, and sink reactions. An exchange reaction is a reversible, unbalanced reaction that adds to or removes an extracellular metabolite from the extracellular compartment. A demand reaction is an irreversible reaction that consumes an intracellular metabolite. A sink is similar to an exchange but specifically for intracellular metabolites, i.e., a reversible reaction that adds or removes an intracellular metabolite. If you set the reaction `type` to something else, you must specify the desired identifier of the created reaction along with its upper and lower bound. The name will be given by the metabolite name and the given `type`. Parameters ---------- metabolite : cobra.Metabolite Any given metabolite. The compartment is not checked but you are encouraged to stick to the definition of exchanges and sinks. type : str, {"exchange", "demand", "sink"} Using one of the pre-defined reaction types is easiest. If you want to create your own kind of boundary reaction choose any other string, e.g., 'my-boundary'. reaction_id : str, optional The ID of the resulting reaction. This takes precedence over the auto-generated identifiers but beware that it might make boundary reactions harder to identify afterwards when using `model.boundary` or specifically `model.exchanges` etc. lb : float, optional The lower bound of the resulting reaction. ub : float, optional The upper bound of the resulting reaction. sbo_term : str, optional A correct SBO term is set for the available types. If a custom type is chosen, a suitable SBO term should also be set. Returns ------- cobra.Reaction The created boundary reaction. Examples -------- >>> import cobra.test >>> model = cobra.test.create_test_model("textbook") >>> demand = model.add_boundary(model.metabolites.atp_c, type="demand") >>> demand.id 'DM_atp_c' >>> demand.name 'ATP demand' >>> demand.bounds (0, 1000.0) >>> demand.build_reaction_string() 'atp_c --> ' """ ub = configuration.upper_bound if ub is None else ub lb = configuration.lower_bound if lb is None else lb types = { "exchange": ("EX", lb, ub, sbo_terms["exchange"]), "demand": ("DM", 0, ub, sbo_terms["demand"]), "sink": ("SK", lb, ub, sbo_terms["sink"]), } if type == "exchange": external = find_external_compartment(self) if metabolite.compartment != external: raise ValueError( "The metabolite is not an external metabolite" " (compartment is `%s` but should be `%s`). " "Did you mean to add a demand or sink? " "If not, either change its compartment or " "rename the model compartments to fix this." % (metabolite.compartment, external) ) if type in types: prefix, lb, ub, default_term = types[type] if reaction_id is None: reaction_id = "{}_{}".format(prefix, metabolite.id) if sbo_term is None: sbo_term = default_term if reaction_id is None: raise ValueError( "Custom types of boundary reactions require a custom " "identifier. Please set the `reaction_id`." ) if reaction_id in self.reactions: raise ValueError( "Boundary reaction '{}' already exists.".format(reaction_id) ) name = "{} {}".format(metabolite.name, type) rxn = Reaction(id=reaction_id, name=name, lower_bound=lb, upper_bound=ub) rxn.add_metabolites({metabolite: -1}) if sbo_term: rxn.annotation["sbo"] = sbo_term self.add_reactions([rxn]) return rxn
[docs] def add_reactions(self, reaction_list): """Add reactions to the model. Reactions with identifiers identical to a reaction already in the model are ignored. The change is reverted upon exit when using the model as a context. Parameters ---------- reaction_list : list A list of `cobra.Reaction` objects """ def existing_filter(rxn): if rxn.id in self.reactions: logger.warning( "Ignoring reaction '%s' since it already exists.", rxn.id ) return False return True # First check whether the reactions exist in the model. pruned = DictList(filter(existing_filter, reaction_list)) context = get_context(self) # Add reactions. Also take care of genes and metabolites in the loop. for reaction in pruned: reaction._model = self # Build a `list()` because the dict will be modified in the loop. for metabolite in list(reaction.metabolites): # TODO: Should we add a copy of the metabolite instead? if metabolite not in self.metabolites: self.add_metabolites(metabolite) # A copy of the metabolite exists in the model, the reaction # needs to point to the metabolite in the model. else: # FIXME: Modifying 'private' attributes is horrible. stoichiometry = reaction._metabolites.pop(metabolite) model_metabolite = self.metabolites.get_by_id(metabolite.id) reaction._metabolites[model_metabolite] = stoichiometry model_metabolite._reaction.add(reaction) if context: context(partial(model_metabolite._reaction.remove, reaction)) for gene in list(reaction._genes): # If the gene is not in the model, add it if not self.genes.has_id(gene.id): self.genes += [gene] gene._model = self if context: # Remove the gene later context(partial(self.genes.__isub__, [gene])) context(partial(setattr, gene, "_model", None)) # Otherwise, make the gene point to the one in the model else: model_gene = self.genes.get_by_id(gene.id) if model_gene is not gene: reaction._dissociate_gene(gene) reaction._associate_gene(model_gene) self.reactions += pruned if context: context(partial(self.reactions.__isub__, pruned)) # from cameo ... self._populate_solver(pruned)
[docs] def remove_reactions(self, reactions, remove_orphans=False): """Remove reactions from the model. The change is reverted upon exit when using the model as a context. Parameters ---------- reactions : list A list with reactions (`cobra.Reaction`), or their id's, to remove remove_orphans : bool Remove orphaned genes and metabolites from the model as well """ if isinstance(reactions, string_types) or hasattr(reactions, "id"): warn("need to pass in a list") reactions = [reactions] context = get_context(self) for reaction in reactions: # Make sure the reaction is in the model try: reaction = self.reactions[self.reactions.index(reaction)] except ValueError: warn("%s not in %s" % (reaction, self)) else: forward = reaction.forward_variable reverse = reaction.reverse_variable if context: obj_coef = reaction.objective_coefficient if obj_coef != 0: context( partial( self.solver.objective.set_linear_coefficients, {forward: obj_coef, reverse: -obj_coef}, ) ) context(partial(self._populate_solver, [reaction])) context(partial(setattr, reaction, "_model", self)) context(partial(self.reactions.add, reaction)) self.remove_cons_vars([forward, reverse]) self.reactions.remove(reaction) reaction._model = None for met in reaction._metabolites: if reaction in met._reaction: met._reaction.remove(reaction) if context: context(partial(met._reaction.add, reaction)) if remove_orphans and len(met._reaction) == 0: self.remove_metabolites(met) for gene in reaction._genes: if reaction in gene._reaction: gene._reaction.remove(reaction) if context: context(partial(gene._reaction.add, reaction)) if remove_orphans and len(gene._reaction) == 0: self.genes.remove(gene) if context: context(partial(self.genes.add, gene)) # remove reference to the reaction in all groups associated_groups = self.get_associated_groups(reaction) for group in associated_groups: group.remove_members(reaction)
[docs] def add_groups(self, group_list): """Add groups to the model. Groups with identifiers identical to a group already in the model are ignored. If any group contains members that are not in the model, these members are added to the model as well. Only metabolites, reactions, and genes can have groups. Parameters ---------- group_list : list A list of `cobra.Group` objects to add to the model. """ def existing_filter(group): if group.id in self.groups: logger.warning("Ignoring group '%s' since it already exists.", group.id) return False return True if isinstance(group_list, string_types) or hasattr(group_list, "id"): warn("need to pass in a list") group_list = [group_list] pruned = DictList(filter(existing_filter, group_list)) for group in pruned: group._model = self for member in group.members: # If the member is not associated with the model, add it if isinstance(member, Metabolite): if member not in self.metabolites: self.add_metabolites([member]) if isinstance(member, Reaction): if member not in self.reactions: self.add_reactions([member]) # TODO(midnighter): `add_genes` method does not exist. # if isinstance(member, Gene): # if member not in self.genes: # self.add_genes([member]) self.groups += [group]
[docs] def remove_groups(self, group_list): """Remove groups from the model. Members of each group are not removed from the model (i.e. metabolites, reactions, and genes in the group stay in the model after any groups containing them are removed). Parameters ---------- group_list : list A list of `cobra.Group` objects to remove from the model. """ if isinstance(group_list, string_types) or hasattr(group_list, "id"): warn("need to pass in a list") group_list = [group_list] for group in group_list: # make sure the group is in the model if group.id not in self.groups: logger.warning("%r not in %r. Ignored.", group, self) else: self.groups.remove(group) group._model = None
[docs] def get_associated_groups(self, element): """Returns a list of groups that an element (reaction, metabolite, gene) is associated with. Parameters ---------- element: `cobra.Reaction`, `cobra.Metabolite`, or `cobra.Gene` Returns ------- list of `cobra.Group` All groups that the provided object is a member of """ # check whether the element is associated with the model return [g for g in self.groups if element in g.members]
[docs] def add_cons_vars(self, what, **kwargs): """Add constraints and variables to the model's mathematical problem. Useful for variables and constraints that can not be expressed with reactions and simple lower and upper bounds. Additions are reversed upon exit if the model itself is used as context. Parameters ---------- what : list or tuple of optlang variables or constraints. The variables or constraints to add to the model. Must be of class `optlang.interface.Variable` or `optlang.interface.Constraint`. **kwargs : keyword arguments Passed to solver.add() """ add_cons_vars_to_problem(self, what, **kwargs)
[docs] def remove_cons_vars(self, what): """Remove variables and constraints from the model's mathematical problem. Remove variables and constraints that were added directly to the model's underlying mathematical problem. Removals are reversed upon exit if the model itself is used as context. Parameters ---------- what : list or tuple of optlang variables or constraints. The variables or constraints to add to the model. Must be of class `optlang.interface.Variable` or `optlang.interface.Constraint`. """ remove_cons_vars_from_problem(self, what)
@property
[docs] def problem(self): """The interface to the model's underlying mathematical problem. Solutions to cobra models are obtained by formulating a mathematical problem and solving it. Cobrapy uses the optlang package to accomplish that and with this property you can get access to the problem interface directly. Returns ------- optlang.interface The problem interface that defines methods for interacting with the problem and associated solver directly. """ return self.solver.interface
@property
[docs] def variables(self): """The mathematical variables in the cobra model. In a cobra model, most variables are reactions. However, for specific use cases, it may also be useful to have other types of variables. This property defines all variables currently associated with the model's problem. Returns ------- optlang.container.Container A container with all associated variables. """ return self.solver.variables
@property
[docs] def constraints(self): """The constraints in the cobra model. In a cobra model, most constraints are metabolites and their stoichiometries. However, for specific use cases, it may also be useful to have other types of constraints. This property defines all constraints currently associated with the model's problem. Returns ------- optlang.container.Container A container with all associated constraints. """ return self.solver.constraints
@property
[docs] def boundary(self): """Boundary reactions in the model. Reactions that either have no substrate or product. """ return [rxn for rxn in self.reactions if rxn.boundary]
@property
[docs] def exchanges(self): """Exchange reactions in model. Reactions that exchange mass with the exterior. Uses annotations and heuristics to exclude non-exchanges such as sink reactions. """ return find_boundary_types(self, "exchange", None)
@property
[docs] def demands(self): """Demand reactions in model. Irreversible reactions that accumulate or consume a metabolite in the inside of the model. """ return find_boundary_types(self, "demand", None)
@property
[docs] def sinks(self): """Sink reactions in model. Reversible reactions that accumulate or consume a metabolite in the inside of the model. """ return find_boundary_types(self, "sink", None)
[docs] def _populate_solver(self, reaction_list, metabolite_list=None): """Populate attached solver with constraints and variables that model the provided reactions. """ constraint_terms = AutoVivification() to_add = [] if metabolite_list is not None: for met in metabolite_list: to_add += [self.problem.Constraint(Zero, name=met.id, lb=0, ub=0)] self.add_cons_vars(to_add) for reaction in reaction_list: if reaction.id not in self.variables: forward_variable = self.problem.Variable(reaction.id) reverse_variable = self.problem.Variable(reaction.reverse_id) self.add_cons_vars([forward_variable, reverse_variable]) else: reaction = self.reactions.get_by_id(reaction.id) forward_variable = reaction.forward_variable reverse_variable = reaction.reverse_variable for metabolite, coeff in six.iteritems(reaction.metabolites): if metabolite.id in self.constraints: constraint = self.constraints[metabolite.id] else: constraint = self.problem.Constraint( Zero, name=metabolite.id, lb=0, ub=0 ) self.add_cons_vars(constraint, sloppy=True) constraint_terms[constraint][forward_variable] = coeff constraint_terms[constraint][reverse_variable] = -coeff self.solver.update() for reaction in reaction_list: reaction = self.reactions.get_by_id(reaction.id) reaction.update_variable_bounds() for constraint, terms in six.iteritems(constraint_terms): constraint.set_linear_coefficients(terms)
[docs] def slim_optimize(self, error_value=float("nan"), message=None): """Optimize model without creating a solution object. Creating a full solution object implies fetching shadow prices and flux values for all reactions and metabolites from the solver object. This necessarily takes some time and in cases where only one or two values are of interest, it is recommended to instead use this function which does not create a solution object returning only the value of the objective. Note however that the `optimize()` function uses efficient means to fetch values so if you need fluxes/shadow prices for more than say 4 reactions/metabolites, then the total speed increase of `slim_optimize` versus `optimize` is expected to be small or even negative depending on how you fetch the values after optimization. Parameters ---------- error_value : float, None The value to return if optimization failed due to e.g. infeasibility. If None, raise `OptimizationError` if the optimization fails. message : string Error message to use if the model optimization did not succeed. Returns ------- float The objective value. """ self.solver.optimize() if self.solver.status == optlang.interface.OPTIMAL: return self.solver.objective.value elif error_value is not None: return error_value else: assert_optimal(self, message)
[docs] def optimize(self, objective_sense=None, raise_error=False): """ Optimize the model using flux balance analysis. Parameters ---------- objective_sense : {None, 'maximize' 'minimize'}, optional Whether fluxes should be maximized or minimized. In case of None, the previous direction is used. raise_error : bool If true, raise an OptimizationError if solver status is not optimal. Notes ----- Only the most commonly used parameters are presented here. Additional parameters for cobra.solvers may be available and specified with the appropriate keyword argument. """ original_direction = self.objective.direction self.objective.direction = {"maximize": "max", "minimize": "min"}.get( objective_sense, original_direction ) self.slim_optimize() solution = get_solution(self, raise_error=raise_error) self.objective.direction = original_direction return solution
[docs] def repair(self, rebuild_index=True, rebuild_relationships=True): """Update all indexes and pointers in a model Parameters ---------- rebuild_index : bool rebuild the indices kept in reactions, metabolites and genes rebuild_relationships : bool reset all associations between genes, metabolites, model and then re-add them. """ if rebuild_index: # DictList indexes self.reactions._generate_index() self.metabolites._generate_index() self.genes._generate_index() self.groups._generate_index() if rebuild_relationships: for met in self.metabolites: met._reaction.clear() for gene in self.genes: gene._reaction.clear() for rxn in self.reactions: for met in rxn._metabolites: met._reaction.add(rxn) for gene in rxn._genes: gene._reaction.add(rxn) # point _model to self for l in (self.reactions, self.genes, self.metabolites, self.groups): for e in l: e._model = self
@property
[docs] def objective(self): """Get or set the solver objective Before introduction of the optlang based problems, this function returned the objective reactions as a list. With optlang, the objective is not limited a simple linear summation of individual reaction fluxes, making that return value ambiguous. Henceforth, use `cobra.util.solver.linear_reaction_coefficients` to get a dictionary of reactions with their linear coefficients (empty if there are none) The set value can be dictionary (reactions as keys, linear coefficients as values), string (reaction identifier), int (reaction index), Reaction or problem.Objective or sympy expression directly interpreted as objectives. When using a `HistoryManager` context, this attribute can be set temporarily, reversed when the exiting the context. """ return self.solver.objective
@objective.setter def objective(self, value): if isinstance(value, Basic): value = self.problem.Objective(value, sloppy=False) if not isinstance(value, (dict, optlang.interface.Objective)): try: reactions = self.reactions.get_by_any(value) except KeyError: raise ValueError("invalid objective") value = {rxn: 1 for rxn in reactions} set_objective(self, value, additive=False) @property
[docs] def objective_direction(self): """ Get or set the objective direction. When using a `HistoryManager` context, this attribute can be set temporarily, reversed when exiting the context. """ return self.solver.objective.direction
@objective_direction.setter @resettable def objective_direction(self, value): value = value.lower() if value.startswith("max"): self.solver.objective.direction = "max" elif value.startswith("min"): self.solver.objective.direction = "min" else: raise ValueError("Unknown objective direction '{}'.".format(value))
[docs] def summary(self, solution=None, fva=None): """ Create a summary of the exchange fluxes of the model. Parameters ---------- solution : cobra.Solution, optional A previous model solution to use for generating the summary. If ``None``, the summary method will generate a parsimonious flux distribution (default None). fva : pandas.DataFrame or float, optional Whether or not to include flux variability analysis in the output. If given, `fva` should either be a previous FVA solution matching the model or a float between 0 and 1 representing the fraction of the optimum objective to be searched (default None). Returns ------- cobra.ModelSummary See Also -------- Reaction.summary Metabolite.summary """ from cobra.summary import ModelSummary return ModelSummary(model=self, solution=solution, fva=fva)
[docs] def __enter__(self): """Record all future changes to the model, undoing them when a call to __exit__ is received""" # Create a new context and add it to the stack try: self._contexts.append(HistoryManager()) except AttributeError: self._contexts = [HistoryManager()] return self
[docs] def __exit__(self, type, value, traceback): """Pop the top context manager and trigger the undo functions""" context = self._contexts.pop() context.reset()
[docs] def merge(self, right, prefix_existing=None, inplace=True, objective="left"): """Merge two models to create a model with the reactions from both models. Custom constraints and variables from right models are also copied to left model, however note that, constraints and variables are assumed to be the same if they have the same name. right : cobra.Model The model to add reactions from prefix_existing : string Prefix the reaction identifier in the right that already exist in the left model with this string. inplace : bool Add reactions from right directly to left model object. Otherwise, create a new model leaving the left model untouched. When done within the model as context, changes to the models are reverted upon exit. objective : string One of 'left', 'right' or 'sum' for setting the objective of the resulting model to that of the corresponding model or the sum of both. """ if inplace: new_model = self else: new_model = self.copy() new_model.id = "{}_{}".format(self.id, right.id) new_reactions = deepcopy(right.reactions) if prefix_existing is not None: existing = new_reactions.query(lambda rxn: rxn.id in self.reactions) for reaction in existing: reaction.id = "{}{}".format(prefix_existing, reaction.id) new_model.add_reactions(new_reactions) interface = new_model.problem new_vars = [ interface.Variable.clone(v) for v in right.variables if v.name not in new_model.variables ] new_model.add_cons_vars(new_vars) new_cons = [ interface.Constraint.clone(c, model=new_model.solver) for c in right.constraints if c.name not in new_model.constraints ] new_model.add_cons_vars(new_cons, sloppy=True) new_model.objective = dict( left=self.objective, right=right.objective, sum=self.objective.expression + right.objective.expression, )[objective] return new_model
[docs] def _repr_html_(self): return """ <table> <tr> <td><strong>Name</strong></td> <td>{name}</td> </tr><tr> <td><strong>Memory address</strong></td> <td>{address}</td> </tr><tr> <td><strong>Number of metabolites</strong></td> <td>{num_metabolites}</td> </tr><tr> <td><strong>Number of reactions</strong></td> <td>{num_reactions}</td> </tr><tr> <td><strong>Number of groups</strong></td> <td>{num_groups}</td> </tr><tr> <td><strong>Objective expression</strong></td> <td>{objective}</td> </tr><tr> <td><strong>Compartments</strong></td> <td>{compartments}</td> </tr> </table>""".format( name=self.id, address="0x0%x" % id(self), num_metabolites=len(self.metabolites), num_reactions=len(self.reactions), num_groups=len(self.groups), objective=format_long_string(str(self.objective.expression), 100), compartments=", ".join( v if v else k for k, v in iteritems(self.compartments)
), )