def pfba(model, fraction_of_optimum=1.0, objective=None, reactions=None):
    """Perform basic pFBA (parsimonious Enzyme Usage Flux Balance Analysis)
    to minimize total flux.

    pFBA [1] adds the minimization of all fluxes the the objective of the
    model. This approach is motivated by the idea that high fluxes have a
    higher enzyme turn-over and that since producing enzymes is costly,
    the cell will try to minimize overall flux while still maximizing the
    original objective function, e.g. the growth rate.

    Parameters
    ----------
    model : cobra.Model
        The model
    fraction_of_optimum : float, optional
        Fraction of optimum which must be maintained. The original objective
        reaction is constrained to be greater than maximal_value *
        fraction_of_optimum.
    objective : dict or model.problem.Objective
        A desired objective to use during optimization in addition to the
        pFBA objective. Dictionaries (reaction as key, coefficient as value)
        can be used for linear objectives.
    reactions : iterable
        List of reactions or reaction identifiers. Implies `return_frame` to
        be true. Only return fluxes for the given reactions. Faster than
        fetching all fluxes if only a few are needed.

    Returns
    -------
    cobra.Solution
        The solution object to the optimized model with pFBA constraints added.

    References
    ----------
    .. [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A.,
       Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data
       from evolved E. coli are consistent with computed optimal growth from
       genome-scale models. Molecular Systems Biology, 6,
       390. doi:10.1038/msb.2010.47

    """
    reactions = model.reactions if reactions is None \
        else model.reactions.get_by_any(reactions)
    with model as m:
        add_pfba(m, objective=objective,
                 fraction_of_optimum=fraction_of_optimum)
        m.slim_optimize(error_value=None)
        solution = get_solution(m, reactions=reactions)
    return solution
def pfba(model, fraction_of_optimum=1.0, objective=None, reactions=None):
    """Perform basic pFBA (parsimonious Enzyme Usage Flux Balance Analysis)
    to minimize total flux.

    pFBA [1] adds the minimization of all fluxes the the objective of the
    model. This approach is motivated by the idea that high fluxes have a
    higher enzyme turn-over and that since producing enzymes is costly,
    the cell will try to minimize overall flux while still maximizing the
    original objective function, e.g. the growth rate.

    Parameters
    ----------
    model : cobra.Model
        The model
    fraction_of_optimum : float, optional
        Fraction of optimum which must be maintained. The original objective
        reaction is constrained to be greater than maximal_value *
        fraction_of_optimum.
    objective : dict or model.problem.Objective
        A desired objective to use during optimization in addition to the
        pFBA objective. Dictionaries (reaction as key, coefficient as value)
        can be used for linear objectives.
    reactions : iterable
        List of reactions or reaction identifiers. Implies `return_frame` to
        be true. Only return fluxes for the given reactions. Faster than
        fetching all fluxes if only a few are needed.

    Returns
    -------
    cobra.Solution
        The solution object to the optimized model with pFBA constraints added.

    References
    ----------
    .. [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A.,
       Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data
       from evolved E. coli are consistent with computed optimal growth from
       genome-scale models. Molecular Systems Biology, 6,
       390. doi:10.1038/msb.2010.47

    """
    reactions = model.reactions if reactions is None \
        else model.reactions.get_by_any(reactions)
    with model as m:
        add_pfba(m, objective=objective,
                 fraction_of_optimum=fraction_of_optimum)
        m.slim_optimize(error_value=None)
        solution = get_solution(m, reactions=reactions)
    return solution
def pfba ( model , fraction_of_optimum = 1.0 , objective = None , reactions = None ) : reactions = model . reactions if reactions is None else model . reactions . get_by_any ( reactions ) with model as m : add_pfba ( m , objective = objective , fraction_of_optimum = fraction_of_optimum ) m . slim_optimize ( error_value = None ) solution = get_solution ( m , reactions = reactions ) return solution
Perform basic pFBA (parsimonious Enzyme Usage Flux Balance Analysis)
    to minimize total flux.

    pFBA [1] adds the minimization of all fluxes the the objective of the
    model. This approach is motivated by the idea that high fluxes have a
    higher enzyme turn-over and that since producing enzymes is costly,
    the cell will try to minimize overall flux while still maximizing the
    original objective function, e.g. the growth rate.

    Parameters
    ----------
    model : cobra.Model
        The model
    fraction_of_optimum : float, optional
        Fraction of optimum which must be maintained. The original objective
        reaction is constrained to be greater than maximal_value *
        fraction_of_optimum.
    objective : dict or model.problem.Objective
        A desired objective to use during optimization in addition to the
        pFBA objective. Dictionaries (reaction as key, coefficient as value)
        can be used for linear objectives.
    reactions : iterable
        List of reactions or reaction identifiers. Implies `return_frame` to
        be true. Only return fluxes for the given reactions. Faster than
        fetching all fluxes if only a few are needed.

    Returns
    -------
    cobra.Solution
        The solution object to the optimized model with pFBA constraints added.

    References
    ----------
    .. [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A.,
       Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data
       from evolved E. coli are consistent with computed optimal growth from
       genome-scale models. Molecular Systems Biology, 6,
       390. doi:10.1038/msb.2010.47
Perform basic pFBA ( parsimonious Enzyme Usage Flux Balance Analysis ) to minimize total flux .