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

# # Predict expression modulation targets
# 
# Cameo provides algorithms to search for genes or reactions that can be over- or down-regulated in order to achieve a given biological objective.

# In[1]:


from cameo import models
from cameo.visualization.plotting.with_plotly import PlotlyPlotter


# Load the E. coli core model.

# In[2]:


model = models.bigg.e_coli_core
plotter = PlotlyPlotter()


# ## Flux Scanning based on Enforced Objective Flux

# In[3]:


from cameo.strain_design.deterministic.flux_variability_based import FSEOF


# In[4]:


fseof = FSEOF(model)


# In[5]:


fseof.run(target=model.reactions.EX_succ_e)


# ## Differential flux variability analysis
# 
# Compares flux ranges of a reference model to a set of models that have been parametrized to lie on a grid of evenly spaced points in the *n*-dimensional production envelope (*n* being the number of reaction bounds to be varied).

# In[6]:


from cameo.flux_analysis.analysis import phenotypic_phase_plane
from cameo.strain_design.deterministic import DifferentialFVA


# ### Succinate production

# The production envelope looks like this.

# In[7]:


production_envelope = phenotypic_phase_plane(model, 
                                             variables=[model.reactions.BIOMASS_Ecoli_core_w_GAM],
                                             objective=model.metabolites.succ_e)
production_envelope.plot(plotter, height=400)


# Set up a model that represents a reference state (in this case a model with a constrained growth rate).

# In[8]:


model.reactions.EX_o2_e.lower_bound = 0
reference_model = model.copy()
biomass_rxn = reference_model.reactions.BIOMASS_Ecoli_core_w_GAM
biomass_rxn.lower_bound = 0.
target = reference_model.metabolites.succ_e


# Set up the differential flux variability analysis strain design method.

# In[9]:


diffFVA = DifferentialFVA(design_space_model=model,
                          reference_model=reference_model,
                          objective=target,
                          variables=[biomass_rxn],
                          normalize_ranges_by=biomass_rxn,
                          points=10)


# Run differential flux variability analysis (only on the surface of the production envelope)

# In[10]:


result = diffFVA.run(surface_only=True)


# In[11]:


result.solutions


# In[14]:


result.plot(plotter, 5, variables=['FBP', 'G6PDH2r', 'PGL', 'PGK'])


# In[13]:


result.display_on_map(2, map_name="iJO1366.Central metabolism")