# 17.1. cobra¶

## 17.1.3. Package Contents¶

class cobra.Configuration

Bases: six.with_metaclass()

Define the configuration to be singleton based.

class cobra.DictList(*args)

Bases: list

A combined dict and list

This object behaves like a list, but has the O(1) speed benefits of a dict when looking up elements by their id.

has_id(self, id)
_check(self, id)

make sure duplicate id’s are not added. This function is called before adding in elements.

_generate_index(self)

rebuild the _dict index

get_by_id(self, id)

return the element with a matching id

list_attr(self, attribute)

return a list of the given attribute for every object

get_by_any(self, iterable)

Get a list of members using several different ways of indexing

Parameters: iterable (list (if not, turned into single element list)) – list where each element is either int (referring to an index in in this DictList), string (a id of a member in this DictList) or member of this DictList for pass-through a list of members list
query(self, search_function, attribute=None)

Query the list

Parameters: search_function (a string, regular expression or function) – Used to find the matching elements in the list. - a regular expression (possibly compiled), in which case the given attribute of the object should match the regular expression. - a function which takes one argument and returns True for desired values attribute (string or None) – the name attribute of the object to passed as argument to the search_function. If this is None, the object itself is used. a new list of objects which match the query DictList

Examples

>>> import cobra.test
>>> model = cobra.test.create_test_model('textbook')
>>> model.reactions.query(lambda x: x.boundary)
>>> import re
>>> regex = re.compile('^g', flags=re.IGNORECASE)
>>> model.metabolites.query(regex, attribute='name')

_replace_on_id(self, new_object)

Replace an object by another with the same id.

append(self, object)

append object to end

union(self, iterable)

extend(self, iterable)

extend list by appending elements from the iterable

_extend_nocheck(self, iterable)

extends without checking for uniqueness

This function should only be used internally by DictList when it can guarantee elements are already unique (as in when coming from self or other DictList). It will be faster because it skips these checks.

__sub__(self, other)

x.__sub__(y) <==> x - y

Parameters: other (iterable) – other must contain only unique id’s present in the list
__isub__(self, other)

x.__sub__(y) <==> x -= y

Parameters: other (iterable) – other must contain only unique id’s present in the list
__add__(self, other)

Parameters: other (iterable) – other must contain only unique id’s which do not intersect with self
__iadd__(self, other)

Parameters: other (iterable) – other must contain only unique id’s whcih do not intersect with self
__reduce__(self)
__getstate__(self)

gets internal state

This is only provided for backwards compatibility so older versions of cobrapy can load pickles generated with cobrapy. In reality, the “_dict” state is ignored when loading a pickle

__setstate__(self, state)

sets internal state

Ignore the passed in state and recalculate it. This is only for compatibility with older pickles which did not correctly specify the initialization class

index(self, id, *args)

Determine the position in the list

id: A string or a Object

__contains__(self, object)

DictList.__contains__(object) <==> object in DictList

object: str or Object

__copy__(self)
insert(self, index, object)

insert object before index

pop(self, *args)

remove and return item at index (default last).

add(self, x)

remove(self, x)

Warning

Internal use only

reverse(self)

reverse IN PLACE

sort(self, cmp=None, key=None, reverse=False)

stable sort IN PLACE

cmp(x, y) -> -1, 0, 1

__getitem__(self, i)
__setitem__(self, i, y)
__delitem__(self, index)
__getslice__(self, i, j)
__setslice__(self, i, j, y)
__delslice__(self, i, j)
__getattr__(self, attr)
__dir__(self)
class cobra.Gene(id=None, name='', functional=True)

A Gene in a cobra model

Parameters: id (string) – The identifier to associate the gene with name (string) – A longer human readable name for the gene functional (bool) – Indicates whether the gene is functional. If it is not functional then it cannot be used in an enzyme complex nor can its products be used.
functional

A flag indicating if the gene is functional.

Changing the flag is reverted upon exit if executed within the model as context.

knock_out(self)

Knockout gene by marking it as non-functional and setting all associated reactions bounds to zero.

The change is reverted upon exit if executed within the model as context.

remove_from_model(self, model=None, make_dependent_reactions_nonfunctional=True)

Removes the association

Parameters: model (cobra model) – The model to remove the gene from make_dependent_reactions_nonfunctional (bool) – If True then replace the gene with ‘False’ in the gene association, else replace the gene with ‘True’

Deprecated since version 0.4: Use cobra.manipulation.delete_model_genes to simulate knockouts and cobra.manipulation.remove_genes to remove genes from the model.

_repr_html_(self)
class cobra.Metabolite(id=None, formula=None, name='', charge=None, compartment=None)

Metabolite is a class for holding information regarding a metabolite in a cobra.Reaction object.

Parameters: id (str) – the identifier to associate with the metabolite formula (str) – Chemical formula (e.g. H2O) name (str) – A human readable name. charge (float) – The charge number of the metabolite compartment (str or None) – Compartment of the metabolite.
constraint

Get the constraints associated with this metabolite from the solve

Returns: the optlang constraint for this metabolite optlang..Constraint
elements

Dictionary of elements as keys and their count in the metabolite as integer. When set, the formula property is update accordingly

formula_weight

Calculate the formula weight

y

The shadow price for the metabolite in the most recent solution

Shadow prices are computed from the dual values of the bounds in the solution.

shadow_price

The shadow price in the most recent solution.

Shadow price is the dual value of the corresponding constraint in the model.

Warning

• Accessing shadow prices through a Solution object is the safer, preferred, and only guaranteed to be correct way. You can see how to do so easily in the examples.
• Shadow price is retrieved from the currently defined self._model.solver. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for.
• If you modify the underlying model after an optimization, you will retrieve the old optimization values.
Raises: RuntimeError – If the underlying model was never optimized beforehand or the metabolite is not part of a model. OptimizationError – If the solver status is anything other than ‘optimal’.

Examples

>>> import cobra
>>> import cobra.test
>>> model = cobra.test.create_test_model("textbook")
>>> solution = model.optimize()
-0.09166474637510488
-0.091664746375104883

_set_id_with_model(self, value)
remove_from_model(self, destructive=False)

Removes the association from self.model

The change is reverted upon exit when using the model as a context.

Parameters: destructive (bool) – If False then the metabolite is removed from all associated reactions. If True then all associated reactions are removed from the Model.
summary(self, solution=None, threshold=0.01, fva=None, names=False, float_format='{:.3g}'.format)

Create a summary of the producing and consuming fluxes.

This method requires the model for which this metabolite is a part to be solved.

Parameters: solution (cobra.Solution, optional) – A previous model solution to use for generating the summary. If None, the summary method will resolve the model. Note that the solution object must match the model, i.e., changes to the model such as changed bounds, added or removed reactions are not taken into account by this method (default None). threshold (float, optional) – Threshold below which fluxes are not reported. May not be smaller than the model tolerance (default 0.01). 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). names (bool, optional) – Emit reaction and metabolite names rather than identifiers (default False). float_format (callable, optional) – Format string for floats (default '{:3G}'.format). cobra.MetaboliteSummary
_repr_html_(self)
class cobra.Model(id_or_model=None, name=None)

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
reactions

A DictList where the key is the reaction identifier and the value a Reaction

Type: DictList
metabolites

A DictList where the key is the metabolite identifier and the value a Metabolite

Type: DictList
genes

A DictList where the key is the gene identifier and the value a Gene

Type: DictList
groups

A DictList where the key is the group identifier and the value a Group

Type: DictList
solution

The last obtained solution from optimizing the model.

Type: Solution
solver

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)

tolerance
description
compartments
medium
problem

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: The problem interface that defines methods for interacting with the problem and associated solver directly. optlang.interface
variables

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: A container with all associated variables. optlang.container.Container
constraints

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: A container with all associated constraints. optlang.container.Container
boundary

Boundary reactions in the model. Reactions that either have no substrate or product.

exchanges

Exchange reactions in model. Reactions that exchange mass with the exterior. Uses annotations and heuristics to exclude non-exchanges such as sink reactions.

demands

Demand reactions in model. Irreversible reactions that accumulate or consume a metabolite in the inside of the model.

sinks

Sink reactions in model. Reversible reactions that accumulate or consume a metabolite in the inside of the model.

objective

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.

objective_direction

Get or set the objective direction.

When using a HistoryManager context, this attribute can be set temporarily, reversed when exiting the context.

__setstate__(self, state)

Make sure all cobra.Objects in the model point to the model.

__getstate__(self)

Get state for serialization.

Ensures that the context stack is cleared prior to serialization, since partial functions cannot be pickled reliably.

get_metabolite_compartments(self)

Return all metabolites’ compartments.

__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.

__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.

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

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) –
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.
add_reaction(self, reaction)

Will add a cobra.Reaction object to the model, if reaction.id is not in self.reactions.

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.

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. The created boundary reaction. cobra.Reaction

Examples

>>> import cobra.test
>>> model = cobra.test.create_test_model("textbook")
>>> demand.id
'DM_atp_c'
>>> demand.name
'ATP demand'
>>> demand.bounds
(0, 1000.0)
>>> demand.build_reaction_string()
'atp_c --> '

add_reactions(self, reaction_list)

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
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
add_groups(self, group_list)

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.
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.
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) – All groups that the provided object is a member of list of cobra.Group
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()
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.
_populate_solver(self, reaction_list, metabolite_list=None)

Populate attached solver with constraints and variables that model the provided reactions.

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. The objective value. float
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.

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.
summary(self, solution=None, threshold=0.01, fva=None, names=False, float_format='{:.3g}'.format)

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 resolve the model. Note that the solution object must match the model, i.e., changes to the model such as changed bounds, added or removed reactions are not taken into account by this method (default None). threshold (float, optional) – Threshold below which fluxes are not reported. May not be smaller than the model tolerance (default 0.01). 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). names (bool, optional) – Emit reaction and metabolite names rather than identifiers (default False). float_format (callable, optional) – Format string for floats (default '{:3G}'.format). cobra.ModelSummary
__enter__(self)

Record all future changes to the model, undoing them when a call to __exit__ is received

__exit__(self, type, value, traceback)

Pop the top context manager and trigger the undo functions

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.
_repr_html_(self)
class cobra.Object(id=None, name='')

Bases: object

Defines common behavior of object in cobra.core

id
_set_id_with_model(self, value)
__getstate__(self)

To prevent excessive replication during deepcopy.

__repr__(self)
__str__(self)
class cobra.Reaction(id=None, name='', subsystem='', lower_bound=0.0, upper_bound=None)

Reaction is a class for holding information regarding a biochemical reaction in a cobra.Model object.

Reactions are by default irreversible with bounds (0.0, cobra.Configuration().upper_bound) if no bounds are provided on creation. To create an irreversible reaction use lower_bound=None, resulting in reaction bounds of (cobra.Configuration().lower_bound, cobra.Configuration().upper_bound).

Parameters: id (string) – The identifier to associate with this reaction name (string) – A human readable name for the reaction subsystem (string) – Subsystem where the reaction is meant to occur lower_bound (float) – The lower flux bound upper_bound (float) – The upper flux bound
reverse_id

Generate the id of reverse_variable from the reaction’s id.

flux_expression

Forward flux expression

Returns: The expression representing the the forward flux (if associated with model), otherwise None. Representing the net flux if model.reversible_encoding == ‘unsplit’ or None if reaction is not associated with a model sympy expression
forward_variable

An optlang variable representing the forward flux

Returns: An optlang variable for the forward flux or None if reaction is not associated with a model. optlang.interface.Variable
reverse_variable

An optlang variable representing the reverse flux

Returns: An optlang variable for the reverse flux or None if reaction is not associated with a model. optlang.interface.Variable
objective_coefficient

Get the coefficient for this reaction in a linear objective (float)

Assuming that the objective of the associated model is summation of fluxes from a set of reactions, the coefficient for each reaction can be obtained individually using this property. A more general way is to use the model.objective property directly.

lower_bound

Get or set the lower bound

Setting the lower bound (float) will also adjust the associated optlang variables associated with the reaction. Infeasible combinations, such as a lower bound higher than the current upper bound will update the other bound.

When using a HistoryManager context, this attribute can be set temporarily, reversed when the exiting the context.

upper_bound

Get or set the upper bound

Setting the upper bound (float) will also adjust the associated optlang variables associated with the reaction. Infeasible combinations, such as a upper bound lower than the current lower bound will update the other bound.

When using a HistoryManager context, this attribute can be set temporarily, reversed when the exiting the context.

bounds

Get or set the bounds directly from a tuple

Convenience method for setting upper and lower bounds in one line using a tuple of lower and upper bound. Invalid bounds will raise an AssertionError.

When using a HistoryManager context, this attribute can be set temporarily, reversed when the exiting the context.

flux

The flux value in the most recent solution.

Flux is the primal value of the corresponding variable in the model.

Warning

• Accessing reaction fluxes through a Solution object is the safer, preferred, and only guaranteed to be correct way. You can see how to do so easily in the examples.
• Reaction flux is retrieved from the currently defined self._model.solver. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for.
• If you modify the underlying model after an optimization, you will retrieve the old optimization values.
Raises: RuntimeError – If the underlying model was never optimized beforehand or the reaction is not part of a model. OptimizationError – If the solver status is anything other than ‘optimal’. AssertionError – If the flux value is not within the bounds.

Examples

>>> import cobra.test
>>> model = cobra.test.create_test_model("textbook")
>>> solution = model.optimize()
>>> model.reactions.PFK.flux
7.477381962160283
>>> solution.fluxes.PFK
7.4773819621602833

reduced_cost

The reduced cost in the most recent solution.

Reduced cost is the dual value of the corresponding variable in the model.

Warning

• Accessing reduced costs through a Solution object is the safer, preferred, and only guaranteed to be correct way. You can see how to do so easily in the examples.
• Reduced cost is retrieved from the currently defined self._model.solver. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for.
• If you modify the underlying model after an optimization, you will retrieve the old optimization values.
Raises: RuntimeError – If the underlying model was never optimized beforehand or the reaction is not part of a model. OptimizationError – If the solver status is anything other than ‘optimal’.

Examples

>>> import cobra.test
>>> model = cobra.test.create_test_model("textbook")
>>> solution = model.optimize()
>>> model.reactions.PFK.reduced_cost
-8.673617379884035e-18
>>> solution.reduced_costs.PFK
-8.6736173798840355e-18

metabolites
genes
gene_reaction_rule
gene_name_reaction_rule

Do NOT use this string for computation. It is intended to give a representation of the rule using more familiar gene names instead of the often cryptic ids.

functional

All required enzymes for reaction are functional.

Returns: True if the gene-protein-reaction (GPR) rule is fulfilled for this reaction, or if reaction is not associated to a model, otherwise False. bool
x

The flux through the reaction in the most recent solution.

Flux values are computed from the primal values of the variables in the solution.

y

The reduced cost of the reaction in the most recent solution.

Reduced costs are computed from the dual values of the variables in the solution.

reversibility

Whether the reaction can proceed in both directions (reversible)

This is computed from the current upper and lower bounds.

boundary

Whether or not this reaction is an exchange reaction.

Returns True if the reaction has either no products or reactants.

model

returns the model the reaction is a part of

__radd__
reactants

Return a list of reactants for the reaction.

products

Return a list of products for the reaction

reaction

compartments

lists compartments the metabolites are in

_set_id_with_model(self, value)
__copy__(self)
__deepcopy__(self, memo)
static _check_bounds(lb, ub)
update_variable_bounds(self)
_update_awareness(self)

Make sure all metabolites and genes that are associated with this reaction are aware of it.

remove_from_model(self, remove_orphans=False)

Removes the reaction from a model.

This removes all associations between a reaction the associated model, metabolites and genes.

The change is reverted upon exit when using the model as a context.

Parameters: remove_orphans (bool) – Remove orphaned genes and metabolites from the model as well
delete(self, remove_orphans=False)

Removes the reaction from a model.

This removes all associations between a reaction the associated model, metabolites and genes.

The change is reverted upon exit when using the model as a context.

Parameters: remove_orphans (bool) – Remove orphaned genes and metabolites from the model as well
__setstate__(self, state)

Probably not necessary to set _model as the cobra.Model that contains self sets the _model attribute for all metabolites and genes in the reaction.

However, to increase performance speed we do want to let the metabolite and gene know that they are employed in this reaction

copy(self)

Copy a reaction

The referenced metabolites and genes are also copied.

__add__(self, other)

The stoichiometry will be the combined stoichiometry of the two reactions, and the gene reaction rule will be both rules combined by an and. All other attributes (i.e. reaction bounds) will match those of the first reaction

__iadd__(self, other)
__sub__(self, other)
__isub__(self, other)
__imul__(self, coefficient)

Scale coefficients in a reaction by a given value

E.g. A -> B becomes 2A -> 2B.

If coefficient is less than zero, the reaction is reversed and the bounds are swapped.

__mul__(self, coefficient)
get_coefficient(self, metabolite_id)

Return the stoichiometric coefficient of a metabolite.

Parameters: metabolite_id (str or cobra.Metabolite) –
get_coefficients(self, metabolite_ids)

Return the stoichiometric coefficients for a list of metabolites.

Parameters: metabolite_ids (iterable) – Containing str or cobra.Metabolites.
add_metabolites(self, metabolites_to_add, combine=True, reversibly=True)

Add metabolites and stoichiometric coefficients to the reaction. If the final coefficient for a metabolite is 0 then it is removed from the reaction.

The change is reverted upon exit when using the model as a context.

Parameters: metabolites_to_add (dict) – Dictionary with metabolite objects or metabolite identifiers as keys and coefficients as values. If keys are strings (name of a metabolite) the reaction must already be part of a model and a metabolite with the given name must exist in the model. combine (bool) – Describes behavior a metabolite already exists in the reaction. True causes the coefficients to be added. False causes the coefficient to be replaced. reversibly (bool) – Whether to add the change to the context to make the change reversibly or not (primarily intended for internal use).
subtract_metabolites(self, metabolites, combine=True, reversibly=True)

Subtract metabolites from a reaction.

That means add the metabolites with -1*coefficient. If the final coefficient for a metabolite is 0 then the metabolite is removed from the reaction.

Notes

• A final coefficient < 0 implies a reactant.
• The change is reverted upon exit when using the model as a context.
Parameters: metabolites (dict) – Dictionary where the keys are of class Metabolite and the values are the coefficients. These metabolites will be added to the reaction. combine (bool) – Describes behavior a metabolite already exists in the reaction. True causes the coefficients to be added. False causes the coefficient to be replaced. reversibly (bool) – Whether to add the change to the context to make the change reversibly or not (primarily intended for internal use).
build_reaction_string(self, use_metabolite_names=False)

Generate a human readable reaction string

check_mass_balance(self)

Compute mass and charge balance for the reaction

returns a dict of {element: amount} for unbalanced elements. “charge” is treated as an element in this dict This should be empty for balanced reactions.

get_compartments(self)

lists compartments the metabolites are in

_associate_gene(self, cobra_gene)

Associates a cobra.Gene object with a cobra.Reaction.

Parameters: cobra_gene (cobra.core.Gene.Gene) –
_dissociate_gene(self, cobra_gene)

Dissociates a cobra.Gene object with a cobra.Reaction.

Parameters: cobra_gene (cobra.core.Gene.Gene) –
knock_out(self)

Knockout reaction by setting its bounds to zero.

build_reaction_from_string(self, reaction_str, verbose=True, fwd_arrow=None, rev_arrow=None, reversible_arrow=None, term_split='+')

Builds reaction from reaction equation reaction_str using parser

Takes a string and using the specifications supplied in the optional arguments infers a set of metabolites, metabolite compartments and stoichiometries for the reaction. It also infers the reversibility of the reaction from the reaction arrow.

Changes to the associated model are reverted upon exit when using the model as a context.

Parameters: reaction_str (string) – a string containing a reaction formula (equation) verbose (bool) – setting verbosity of function fwd_arrow (re.compile) – for forward irreversible reaction arrows rev_arrow (re.compile) – for backward irreversible reaction arrows reversible_arrow (re.compile) – for reversible reaction arrows term_split (string) – dividing individual metabolite entries
summary(self, solution=None, threshold=0.01, fva=None, names=False, float_format='{:.3g}'.format)

Create a summary of the producing and consuming fluxes of the reaction.

Parameters: solution (cobra.Solution, optional) – A previous model solution to use for generating the summary. If None, the summary method will resolve the model. Note that the solution object must match the model, i.e., changes to the model such as changed bounds, added or removed reactions are not taken into account by this method (default None). threshold (float, optional) – Threshold below which fluxes are not reported. May not be smaller than the model tolerance (default 0.01). 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). names (bool, optional) – Emit reaction and metabolite names rather than identifiers (default False). float_format (callable, optional) – Format string for floats (default '{:3G}'.format). cobra.ReactionSummary
__str__(self)
_repr_html_(self)
class cobra.Species(id=None, name=None)

Species is a class for holding information regarding a chemical Species

Parameters: id (string) – An identifier for the chemical species name (string) – A human readable name.
reactions
model
__getstate__(self)

Remove the references to container reactions when serializing to avoid problems associated with recursion.

copy(self)

When copying a reaction, it is necessary to deepcopy the components so the list references aren’t carried over.

Additionally, a copy of a reaction is no longer in a cobra.Model.

This should be fixed with self.__deepcopy__ if possible

cobra.show_versions()

Print dependency information.

cobra.__version__ = 0.15.4[source]
cobra._cobra_path[source]
cobra._warning_base = %s:%s [1;31m%s[0m: %s[source]
cobra._warn_format(message, category, filename, lineno, file=None, line=None)[source]
cobra.formatwarning[source]