Abstract
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 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C 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 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C 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 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).
Highlights
Fluxomics is the omics field that analyses metabolic fluxes which are a close reflection of the metabolic phenotype
13C Metabolic Flux Analysis (13C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism
When a biological system is incubated with a 13C-labeled substrate, 13C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. 13C MFA integrates measurements of 13C enrichment in metabolites to identify the flux distributions consistent with the measured 13C propagation
Summary
Fluxomics is the omics field that analyses metabolic fluxes (i.e., reaction and transport rates in living cells) which are a close reflection of the metabolic phenotype. Unlike other omics data that can be quantified directly, the fluxome can only be estimated through an indirect interpretation of experimental data[1,2,3]. There are two main model-based approaches to quantifying metabolic fluxes, Flux Balance Analysis (FBA) and 13C Metabolic Flux Analysis (13C MFA). Both methods use stoichiometric, thermodynamic and experimental constraints to find the range of feasible fluxes across a metabolic network and find the flux distributions within that space that optimize a given objective function. Both techniques differ in the type of objective function optimized
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