p(13)CMFA: Parsimonious (13)C metabolic flux analysis

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are (13)C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using (13)C Metabolic Flux Analysis ((13)C MFA) to provide quantitative metabolic maps of flux distributions. However, (13)C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious (13)C MFA (p(13)CMFA), an approach that runs a secondary optimization in the (13)C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with (13)C data. As proof of concept, we demonstrate how p(13)CMFA can be used to estimate intracellular flux distributions from (13)C measurements and transcriptomics data. We have implemented p(13)CMFA in Iso2Flux, our in-house developed isotopic steady-state (13)C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).