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, things might run very slow due to our inability to provide access to the proprietary CPLEX solver on a public webserver. Furthermore, Jupyter kernels might crash and restart due to memory limitations on the server.

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

model = models.bigg.iJO1366
plotter = PlotlyPlotter()

wt_solution = model.optimize()
growth = wt_solution.fluxes["BIOMASS_Ec_iJO1366_core_53p95M"]
acetate_production = wt_solution.fluxes["EX_ac_e"]

from cameo import phenotypic_phase_plane

p = phenotypic_phase_plane(model, variables=['BIOMASS_Ec_iJO1366_core_53p95M'], objective='EX_ac_e')
p.plot(plotter, points=[(growth, acetate_production)])