code: def flux_minimization(model, fixed, solver, weights={}):
      
  
      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)
docs: 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.