def flux_minimization(model, fixed, solver, weights={}):
    """Minimize flux of all reactions while keeping certain fluxes fixed.

    The fixed reactions are given in a dictionary as reaction id
    to value mapping. The weighted L1-norm of the fluxes is minimized.

    Args:
        model: MetabolicModel to solve.
        fixed: dict of additional lower bounds on reaction fluxes.
        solver: LP solver instance to use.
        weights: dict of weights on the L1-norm terms.

    Returns:
        An iterator of reaction ID and reaction flux pairs.
    """

    fba = FluxBalanceProblem(model, solver)

    for reaction_id, value in iteritems(fixed):
        flux = fba.get_flux_var(reaction_id)
        fba.prob.add_linear_constraints(flux >= value)

    fba.minimize_l1()

    return ((reaction_id, fba.get_flux(reaction_id))
            for reaction_id in model.reactions)