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

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from cameo import load_model


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model = load_model("iJR904")


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model


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from cameo.flux_analysis import simulation


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help(simulation)


# Step 1: Simulate the flux distribution of the Wild-Type
# ----------------------------------------------------
# 
# FBA and PFBA can be used to compute flux distributions for the Wild-Type (model as it as been described) as well as knockouts (next session)

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get_ipython().run_line_magic('time', '')
fba_result = simulation.fba(model)


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get_ipython().run_line_magic('time', '')
pfba_result = simulation.pfba(model)


# Setp 2: Simulate knockouts phenotypes
# -----------------------------------
# 
# Although PFBA and FBA can be used to simulate the effect of knockouts, other methods have been proven more valuable for that task: MOMA and ROOM. In *cameo* we implement a linear version of MOMA.
# 
# *******************************************
# Simulating knockouts:
# 
# * Manipulate the bounds of the reaction (or use the shorthand method knock_out)

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model.reactions.PGI


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model.reactions.PGI.knock_out()
model.reactions.PGI


# * Simulate using different methods:

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get_ipython().run_line_magic('time', '')
fba_knockout_result = simulation.fba(model)
fba_knockout_result[model.objective]


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pfba_knockout_result = simulation.pfba(model)
pfba_knockout_result[model.objective]


# MOMA and ROOM relly on a reference (wild-type) flux distribution and we can use the one previously computed.
# 
# **Parsimonious FBA references seem to produce better results using this methods**

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lmoma_result["2 * EX_glc_lp_e_rp_"]


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get_ipython().run_line_magic('time', '')
lmoma_result = simulation.lmoma(model, reference=pfba_result.fluxes)
lmoma_result[model.objective]


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get_ipython().run_line_magic('time', '')
room_result = simulation.room(model, reference=pfba_result.fluxes)
room_result[model.objective]


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room_result


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