Analyzing models

cameo uses the model data structures defined by cobrapy, our favorite COnstraints-Based Reconstruction and Analysis tool for Python. cameo is thus 100% compatible with cobrapy. For efficiency reasons though cameo implements its own analysis methods that take advantage of a more advanced solver interface.

from cameo import models
model = models.bigg.e_coli_core

Flux Variability Analysis

Flux variability analysis (FVA) enables the computation of lower and upper bounds of reaction fluxes.

from cameo import flux_variability_analysis
fva_result = flux_variability_analysis(model)
fva_result.data_frame
lower_bound upper_bound
ACALD -20.000000 0.000000
ACALDt -20.000000 0.000000
ACKr -20.000000 0.000000
ACONTa 0.000000 20.000000
ACONTb 0.000000 20.000000
ACt2r -20.000000 0.000000
ADK1 0.000000 166.610000
AKGDH 0.000000 20.000000
AKGt2r -10.000000 0.000000
ALCD2x -20.000000 0.000000
ATPM 8.390000 175.000000
ATPS4r -31.610000 150.000000
BIOMASS_Ecoli_core_w_GAM 0.000000 0.873922
CO2t -60.000000 11.104242
CS 0.000000 20.000000
CYTBD 0.000000 120.000000
D_LACt2 -20.000000 0.000000
ENO 0.000000 20.000000
ETOHt2r -20.000000 0.000000
EX_ac_e 0.000000 20.000000
EX_acald_e 0.000000 20.000000
EX_akg_e 0.000000 10.000000
EX_co2_e -11.104242 60.000000
EX_etoh_e 0.000000 20.000000
EX_for_e 0.000000 40.000000
EX_fru_e 0.000000 0.000000
EX_fum_e 0.000000 0.000000
EX_glc__D_e -10.000000 -0.479429
EX_gln__L_e 0.000000 0.000000
EX_glu__L_e 0.000000 10.000000
... ... ...
ME2 0.000000 98.305000
NADH16 0.000000 120.000000
NADTRHD 0.000000 378.220000
NH4t 0.000000 10.000000
O2t 0.000000 60.000000
PDH 0.000000 40.000000
PFK 0.000000 176.610000
PFL 0.000000 40.000000
PGI -50.000000 10.000000
PGK -20.000000 0.000000
PGL 0.000000 60.000000
PGM -20.000000 0.000000
PIt2r 0.000000 3.214895
PPC 0.000000 166.610000
PPCK 0.000000 166.610000
PPS 0.000000 166.610000
PTAr 0.000000 20.000000
PYK 0.000000 176.610000
PYRt2 -20.000000 0.000000
RPE -0.620909 40.000000
RPI -20.000000 0.000000
SUCCt2_2 0.000000 222.146667
SUCCt3 0.000000 222.146667
SUCDi 0.000000 1000.000000
SUCOAS -20.000000 0.000000
TALA -0.154536 20.000000
THD2 0.000000 333.220000
TKT1 -0.154536 20.000000
TKT2 -0.466373 20.000000
TPI -10.000000 10.000000

95 rows × 2 columns

fva_result.plot(index=fva_result.data_frame.index[:25])