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

Load the E. coli core model.

model = models.bigg.e_coli_core

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)
Model: e_coli_core
Enforced objective: EX_succ_e
Primary objective: Maximize -1.0*BIOMASS_Ecoli_core_w_GAM_reverse_712e5 + 1.0*BIOMASS_Ecoli_core_w_GAM

Reaction fluxes

1 2 3 4 5 6 7 8 9 10
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
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
... ... ... ... ... ... ... ... ... ... ...
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
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
EX_succ_e 1.474575 2.949150 4.423725 5.898300 7.372875 8.847450 10.322025 11.796600 13.271175 14.745750

18 rows × 10 columns

Differential flux variability analysis

Compares flux ranges of a reference model to a set of models that have been parameterized 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(height=400)

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

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.

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)

%time result = diffFVA.run(surface_only=True)
result.plot()

CPU times: user 1.49 s, sys: 32.3 ms, total: 1.52 s
Wall time: 2.16 s

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

result
BIOMASS_Ecoli_core_w_GAM: 0.094072DM_succ_e: 9.873904
upper_bound lower_bound gaps normalized_gaps KO flux_reversal suddenly_essential
ACALD -9.10733e-16 -9.10733e-16 0 0 False False True
ACALDt 0 0 0 0 False False True
ACKr -8.69634 -8.69634 0 82.443 False False True
ACONTa 0.101495 0.101495 0 0 False False False
ACONTb 0.101495 0.101495 0 0 False False False
ACt2r -8.69634 -8.69634 0 82.443 False False True
ADK1 0.583116 0.583116 0 0 False False False
... ... ... ... ... ... ... ...
SUCDi 990.126 0 0 0 False False False
SUCOAS 0 -1.88738e-14 0 0 False False True
TALA -0.0168296 -0.0168296 0 -0.141033 False False True
THD2 1.61298 1.61298 0 0 False False False
TKT1 -0.0168296 -0.0168296 0 -0.141033 False False True
TKT2 -0.0507897 -0.0507897 0 -0.425623 False False True
TPI 9.90643 9.90643 0 95.3064 False False False

76 rows × 7 columns

result.display_on_map(5, map_name="iJO1366.Central metabolism")