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.
from cameo import models
from cameo.visualization.plotting.with_plotly import PlotlyPlotter
Load the E. coli core model.
model = models.bigg.e_coli_core
plotter = PlotlyPlotter()
Flux Scanning based on Enforced Objective Flux¶
from cameo.strain_design.deterministic.flux_variability_based import FSEOF
fseof = FSEOF(model)
fseof.run(target=model.reactions.EX_succ_e)
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).
from cameo.flux_analysis.analysis import phenotypic_phase_plane
from cameo.strain_design.deterministic import DifferentialFVA
Succinate production¶
The production envelope looks like this.
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)