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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Gapfillling
GrowMatch and SMILEY are gap-filling algorithms, which try to to make the minimal number of changes to a model and allow it to simulate growth. For more information, see Kumar et al.. Please note that these algorithms are Mixed-Integer Linear Programs, which need solvers such as gurobi or cplex to function correctly.
Step1:  In this model D-Fructose-6-phosphate is an essential metabolite. We will remove all the reactions using it, and at them to a separate model.
Step2:  Now, because of these gaps, the model won't grow.
Step3:  GrowMatch
We will use GrowMatch to add back the minimal number of reactions from this set of "universal" reactions (in this case just the ones we removed) to allow it to grow.
Step4:  We can obtain multiple possible reaction sets by having the algorithm go through multiple iterations.
Step5:  SMILEY
SMILEY is very similar to growMatch, only instead of setting growth as the objective, it sets production of a specific metabolite
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Python Code:
import cobra.test
model = cobra.test.create_test_model("salmonella")
Explanation: Gapfillling
GrowMatch and SMILEY are gap-filling algorithms, which try to to make the minimal number of changes to a model and allow it to simulate growth. For more information, see Kumar et al.. Please note that these algorithms are Mixed-Integer Linear Programs, which need solvers such as gurobi or cplex to function correctly.
End of explanation
# remove some reactions and add them to the universal reactions
Universal = cobra.Model("Universal_Reactions")
for i in [i.id for i in model.metabolites.f6p_c.reactions]:
    reaction = model.reactions.get_by_id(i)
    Universal.add_reaction(reaction.copy())
    reaction.remove_from_model()
Explanation: In this model D-Fructose-6-phosphate is an essential metabolite. We will remove all the reactions using it, and at them to a separate model.
End of explanation
model.optimize().f
Explanation: Now, because of these gaps, the model won't grow.
End of explanation
r = cobra.flux_analysis.growMatch(model, Universal)
for e in r[0]:
    print(e.id)
Explanation: GrowMatch
We will use GrowMatch to add back the minimal number of reactions from this set of "universal" reactions (in this case just the ones we removed) to allow it to grow.
End of explanation
result = cobra.flux_analysis.growMatch(model, Universal,
                                       iterations=4)
for i, entries in enumerate(result):
    print("---- Run %d ----" % (i + 1))
    for e in entries:
        print(e.id)
Explanation: We can obtain multiple possible reaction sets by having the algorithm go through multiple iterations.
End of explanation
r = cobra.flux_analysis.gapfilling.SMILEY(model, "ac_e",
                                          Universal)
for e in r[0]:
    print(e.id)
Explanation: SMILEY
SMILEY is very similar to growMatch, only instead of setting growth as the objective, it sets production of a specific metabolite
End of explanation
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