def flux_randomization(model, threshold, tfba, solver):
    """Find a random flux solution on the boundary of the solution space.

    The reactions in the threshold dictionary are constrained with the
    associated lower bound.

    Args:
        model: MetabolicModel to solve.
        threshold: dict of additional lower bounds on reaction fluxes.
        tfba: If True enable thermodynamic constraints.
        solver: LP solver instance to use.

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

    optimize = {}
    for reaction_id in model.reactions:
        if model.is_reversible(reaction_id):
            optimize[reaction_id] = 2*random.random() - 1.0
        else:
            optimize[reaction_id] = random.random()

    fba = _get_fba_problem(model, tfba, solver)
    for reaction_id, value in iteritems(threshold):
        fba.prob.add_linear_constraints(fba.get_flux_var(reaction_id) >= value)

    fba.maximize(optimize)
    for reaction_id in model.reactions:
        yield reaction_id, fba.get_flux(reaction_id)
Find a random flux solution on the boundary of the solution space.

    The reactions in the threshold dictionary are constrained with the
    associated lower bound.

    Args:
        model: MetabolicModel to solve.
        threshold: dict of additional lower bounds on reaction fluxes.
        tfba: If True enable thermodynamic constraints.
        solver: LP solver instance to use.

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