BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale
Fig 7
Knockout predictions for genome-scale fluxes improve by leveraging BayFlux flux probability distributions.
Here we show the knockout prediction performance for four methods as judged by the distance of the prediction to the experimentally measured flux profile distribution (computed with BayFlux from 13C experimental data). Rather than using single fluxes to determine which method performs better (Fig E in S1 Text), distances between full flux profile distributions comprising all fluxes are calculated through a classical measure of how two probability distributions differ from each other: the multivariate Kullback-Leibler divergence [47] (higher value → worse prediction, lower value → better prediction). The distance between the WT base profile distribution and the KO experimentally observed flux profile distribution is provided for reference. Notice how P-13C MOMA and P-13C ROOM produce smaller distances to the experimental results as compared with MOMA and ROOM, indicating improved predictions. The improvement is particularly pronounced for P-13C MOMA, whereas it is marginal for P-13C ROOM. All distances are shown as relative to the knockout strains (on the left) but flux profiles inhabit a multidimensional space, so similar distances do not mean the distance among them is small (e.g., the fact that the wild type and the P-13C MOMA have a similar distance to the pyk5h knockout does not mean that these two flux distributions are similar to one another).