Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
PLoS Comput Biol. 2019 Sep 6;15(9):e1007310. doi: 10.1371/journal.pcbi.1007310. eCollection 2019 Sep.
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).
解析代谢网络（包括疾病状态和药物诱导应激等）的调控机制依赖于追踪代谢通量。其中最具信息量的预测代谢通量的数据是基于 13C 的代谢组学，它提供了有关碳在中心碳代谢中如何重新分布的信息。此类数据可以使用 13C 代谢通量分析（13C MFA）进行整合，以提供通量分布的定量代谢图谱。然而，在大型代谢网络中或整合少量测量值时，13C MFA 可能无法将解空间缩小到唯一解。在这里，我们提出了简约的 13C MFA（p13CMFA），这是一种在 13C MFA 解空间中运行二次优化的方法，以确定最小化总反应通量的解。此外，可以通过基因表达测量值对通量最小化进行加权，从而实现基因表达数据与 13C 数据的无缝整合。作为概念验证，我们展示了如何使用 p13CMFA 从 13C 测量值和转录组学数据估计细胞内通量分布。我们已经在 Iso2Flux 中实现了 p13CMFA，这是我们内部开发的同位素稳态 13C MFA 软件。源代码可在 GitHub 上免费获取（https://github.com/cfoguet/iso2flux/releases/tag/0.7.2）。
