< Back to Article

Accurate prediction of flux distributions compatible with metabolite concentration effects in genome-scale metabolic networks

Fig 2

Performance of logit regression models for metabolite concentration effects and their implication on flux predictions in E. coli.

A. The histogram illustrates the performance of the logit regression models in predicting metabolite concentration effects, based on their adjusted , for 339 reactions, each with more than 10 values corresponding to different E. coli knock-out strains. Among these, 92 reactions achieved an adjusted greater than 0.5. The respective logit models were in turn used in the constraint-based optimization problem B. Comparison of the predicted flux from the optimization problem with the estimated flux for the phosphoglycerate kinase (PGK_b) reaction, resulting in a Pearson correlation coefficient of 0.90 (p-value = ). C. The histogram presents the number of reactions based on the Pearson correlations between their predicted and estimated fluxes.

doi: https://doi.org/10.1371/journal.pcbi.1014066.g002