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

doi:10.1371/journal.pcbi.1014066

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

Fig 5

Performance of KineFlux to predict flux distribution for unseen conditions in E. coli.

A. Histogram showing the distribution of Pearson correlation coefficients between predicted and estimated fluxes of the reactions across various growth conditions, each associated with different carbon uptake sources [1]. B. Comparison of the predicted and estimated fluxes under the condition GLC_CHEM_mu = 0.21_V from Davidi et al. [1], which corresponds to a chemostat culture with a growth rate of 0.21 using glucose as the carbon source [40]. All flux values are log-transformed, with a small constant ( added to prevent logarithms of zero. Highlighted reactions located farthest from the diagonal, with non-zero predicted fluxes, include: MALS (Malate synthase), ADD (Adenine deaminase), FORtppi (Formate transport via diffusion), FDH4pp (Formate dehydrogenase (quinone-8)), ENO_f (Enolase), TPI_b (Triose-phosphate isomerase), PUNP5_f (Purine-nucleoside phosphorylase (Inosine)), FADRx (FAD reductase), GAPD_f (Glyceraldehyde-3-phosphate dehydrogenase), and PGK_b (Phosphoglycerate kinase).

doi: https://doi.org/10.1371/journal.pcbi.1014066.g005