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Title: p13CMFA: Parsimonious 13C metabolic flux analysis
Author: Foguet Coll, Carles
Jayaraman, Anusha
Marin, Silvia
Selivanov, Vitaly A.
Moreno, Pablo
Messeguer i Peypoch, Ramon
Atauri, Pedro de
Cascante i Serratosa, Marta
Keywords: Metabolisme dels medicaments
Expressió gènica
Drugs metabolism
Gene expression
Issue Date: 6-Sep-2019
Publisher: Public Library of Science (PLoS)
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 (
Note: Reproducció del document publicat a:
It is part of: PLoS Computational Biology, 2019, vol. 15, num. 9, p. e1007310
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ISSN: 1553-734X
Appears in Collections:Articles publicats en revistes (Institut de Biomedicina (IBUB))
Articles publicats en revistes (Bioquímica i Biomedicina Molecular)

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