code: def single_gene_deletion(model, gene_list=None, method="fba", solution=None,
                           processes=None, **kwargs):
      
      return _multi_deletion(
          model, 'gene', element_lists=_element_lists(model.genes, gene_list),
          method=method, solution=solution, processes=processes, **kwargs)
docs: Knock out each gene from a given list.
  
      Parameters
      ----------
      model : cobra.Model
          The metabolic model to perform deletions in.
      gene_list : iterable
          ``cobra.Gene``s to be deleted. If not passed,
          all the genes from the model are used.
      method: {"fba", "moma", "linear moma", "room", "linear room"}, optional
          Method used to predict the growth rate.
      solution : cobra.Solution, optional
          A previous solution to use as a reference for (linear) MOMA or ROOM.
      processes : int, optional
          The number of parallel processes to run. Can speed up the computations
          if the number of knockouts to perform is large. If not passed,
          will be set to the number of CPUs found.
      kwargs :
          Keyword arguments are passed on to underlying simulation functions
          such as ``add_room``.
  
      Returns
      -------
      pandas.DataFrame
          A representation of all single gene deletions. The columns are
          'growth' and 'status', where
  
          index : frozenset([str])
              The gene identifier that was knocked out.
          growth : float
              The growth rate of the adjusted model.
          status : str
              The solution's status.