Predict gene knockout strategies

In cameo we have two ways of predicting gene knockout targets: using evolutionary algorithms (OptGene) or linear programming (OptKnock)

If you're running this notebook on [try.cameo.bio](http://try.cameo.bio), things might run very slow due to our inability to provide access to the proprietary [CPLEX](https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/) solver on a public webserver. Furthermore, Jupyter kernels might crash and restart due to memory limitations on the server.
In [1]:
from cameo import models
In [2]:
model = models.bigg.iJO1366
In [3]:
wt_solution = model.optimize()
growth = wt_solution.fluxes["BIOMASS_Ec_iJO1366_core_53p95M"]
acetate_production = wt_solution.fluxes["EX_ac_e"]
In [4]:
from cameo import phenotypic_phase_plane
In [5]:
p = phenotypic_phase_plane(model, variables=['BIOMASS_Ec_iJO1366_core_53p95M'], objective='EX_ac_e')
p.plot(points=[(growth, acetate_production)])