BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale
Fig 6
Comparison P-13C MOMA and P-13C ROOM predictions with traditional MOMA and ROOM flux predictions for the core metabolic model reactions.
The x axis represent the Euclidean distances for the flux profiles predicted by MOMA, ROOM, P-13C MOMA and P-13C ROOM to the original fluxes calculated by Toya et al. [39] using a core metabolic model (ground truth fluxes, the smaller the Euclidean distance the better the prediction). Since P-13C MOMA and P-13C ROOM yield distributions of predicted flux profiles, the y axis represents the density of distances for these methods. MOMA and ROOM yield a single predicted flux profile, so we plot a single line to represent them. A. Distribution of euclidean distances to Toya et al. pyk5h flux predictions. 23.5% of the P-13C MOMA and 36.5% of the P-13C ROOM prediction distribution were more accurate than the traditional (FBA-based) MOMA and ROOM results, respectively. B. Distribution of euclidean distances to Toya et al. pgi16h flux predictions. 18.5% of the P-13C MOMA and 38.5% of the P-13C ROOM predicted distribution flux profiles were more accurate than the traditional MOMA and ROOM results, respectively.