Define the Metabolite class.
22.214.171.124.1.6.1. Module Contents¶
Metabolite is a class for holding information regarding
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.
Get the constraints associated with this metabolite from the solve
the optlang constraint for this metabolite
- Return type
Dictionary of elements as keys and their count in the metabolite as integer. When set, the formula property is update accordingly
Calculate the formula weight
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.
The shadow price in the most recent solution.
Shadow price is the dual value of the corresponding constraint in the model.
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.
>>> import cobra >>> import cobra.test >>> model = cobra.test.create_test_model("textbook") >>> solution = model.optimize() >>> model.metabolites.glc__D_e.shadow_price -0.09166474637510488 >>> solution.shadow_prices.glc__D_e -0.091664746375104883
Removes the association from self.model
The change is reverted upon exit when using the model as a context.
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, fva=None)¶
Create a summary of the producing and consuming fluxes.
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).
- Return type