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")

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