BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale
Fig 5
Using genome-scale models produces more biologically meaningful solutions.
The results obtained from BayFlux with a core metabolic model (blue) are compared with those obtained from a genome-scale model (orange). Using a genome-scale model produces a narrower flux distribution (higher certainty posterior probability distributions), as informed by a greater amount of biological knowledge encoded in the genome-scale model. Notice too, that certain reactions display very different averages. For example, GLUDY shows very different averages for the genome-scale and core metabolic models, advising caution in assuming strong inferences from 13C MFA since the results may depend significantly on the model used. Additionally, several of the probability distributions are non-Gaussian, which can only be meaningfully represented as a full distribution rather than a point or interval. We show here only reactions which occur in both models, and which show convergence across 4 repeated BayFlux runs (, Gelman-Rubin statistic [42], see main text). Reaction names correspond to Core Metabolic Model 2 (see Materials and methods).