#!/usr/bin/env python
# coding: utf-8

# # 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.](http://dx.doi.org/10.1371/journal.pcbi.1000308). Please note that these algorithms are Mixed-Integer Linear Programs, which need solvers such as gurobi or cplex to function correctly.

# In[1]:


import cobra.test

model = cobra.test.create_test_model("salmonella")


# 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.

# In[2]:


# 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()


# Now, because of these gaps, the model won't grow.

# In[3]:


model.optimize().f


# ## 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.

# In[4]:


r = cobra.flux_analysis.growMatch(model, Universal)
for e in r[0]:
    print(e.id)


# We can obtain multiple possible reaction sets by having the algorithm go through multiple iterations.

# In[5]:


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)


# ## SMILEY
# 
# SMILEY is very similar to growMatch, only instead of setting growth as the objective, it sets production of a specific metabolite

# In[6]:


r = cobra.flux_analysis.gapfilling.SMILEY(model, "ac_e",
                                          Universal)
for e in r[0]:
    print(e.id)