1. cameo-notebooks
  2. 06-predict-gene-modulation-targets.ipynb

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)
Out[5]:
Model: e_coli_core
Enforced objective: EX_succ_e
Primary objective: Maximize 1.0*BIOMASS_Ecoli_core_w_GAM - 1.0*BIOMASS_Ecoli_core_w_GAM_reverse_712e5

Reaction fluxes

1 2 3 4 5 6 7 8 9 10
PFK 7.756941 8.036499 8.242515 8.361048 8.479582 8.598115 8.747793 9.393102 9.830787 9.910940
PGI 5.485630 6.110399 6.513173 6.651876 6.790579 6.929282 7.161108 8.874882 9.965126 9.981645
PGK -16.431132 -16.838737 -17.173618 -17.421989 -17.670359 -17.918730 -18.198431 -18.976723 -19.547784 -19.761991
PGM -15.238892 -15.761645 -16.212407 -16.577535 -16.942662 -17.307790 -17.704415 -18.602292 -19.293292 -19.628048
PPC 3.758320 5.012330 5.878875 6.284516 6.690157 7.095799 7.532996 8.472367 9.615323 11.108854
PPS 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.410338 1.527283
ADK1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.410338 1.527283
ENO 15.238892 15.761645 16.212407 16.577535 16.942662 17.307790 17.704415 18.602292 19.293292 19.628048
SUCCt3 1.474575 2.949150 4.423725 5.898300 7.372875 8.847450 10.322025 11.796600 13.271175 14.745750
THD2 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.119674 2.143848 2.916807 1.535161
TPI 7.756941 8.036499 8.242515 8.361048 8.479582 8.598115 8.747793 9.393102 9.830787 9.910940
FBA 7.756941 8.036499 8.242515 8.361048 8.479582 8.598115 8.747793 9.393102 9.830787 9.910940
FRD7 0.000000 0.000000 0.000000 1.453235 3.175302 4.897369 6.586703 7.755141 9.127842 10.852285
GAPD 16.431132 16.838737 17.173618 17.421989 17.670359 17.918730 18.198431 18.976723 19.547784 19.761991
ICL 0.000000 0.000000 0.386057 1.231342 2.076627 2.921912 3.735322 4.041459 4.143333 3.893465
MALS 0.000000 0.000000 0.386057 1.231342 2.076627 2.921912 3.735322 4.041459 4.143333 3.893465
PDH 8.809270 8.336008 8.179868 8.400957 8.622045 8.843134 9.064539 9.290984 9.107760 8.219084
EX_succ_e 1.474575 2.949150 4.423725 5.898300 7.372875 8.847450 10.322025 11.796600 13.271175 14.745750

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)