BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale
Fig 4
Fluxes obtained from BayFlux using a flux sampling approach are compatible with the optimization results from 13CFLUX2, but offer more information.
Whereas the optimization approach only provides the best fit and confidence intervals, BayFlux supplies the probability distribution of all fluxes compatible with the experimental 13C data (Fig 1). Probability densities (blue), best sample (vertical magenta line), and mean (vertical green line) from BayFlux for ten million flux samples are shown vs. 13CFLUX2 best fit with confidence intervals (in orange) for 5 out of 66 fluxes (see Fig 3 for best fits and best samples for a greater number of reactions). Reaction names correspond to Core Metabolic Model 1 (see Materials and methods). The credible intervals for, e.g., fluxes tca3 and tca4 (see Fig 3) overlap with the 13CFLUX2 best fit and confidence intervals. This shows that the difference is not significant, given the current data, and highlights the importance of quantifying flux uncertainty.