Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes

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• 作者：Jon Pey^a ; Angel Rubio^a ; Constantinos Theodoropoulos^b ; Marta Cascante^c ; ^{martacascante@ub.edu} ; Francisco J. Planes^a ; ^{fplanes@tecnun.es}
• 关键词：Metabolic Flux Analysis ; Isotope Labeling Experiments ; Constraints-based modeling ; Elementary Carbon Modes ; Flux Variability Analysis
• 刊名：Metabolic Engineering
• 出版年：2012
• 期刊代码：98_10967176
• 类别：ce
• 出版时间：July, 2012
• 卷：14
• 期：4
• 页码：344-353
• 文件大小：841 K

摘要

Constraints-based modeling is an emergent area in Systems Biology that includes an increasing set of methods for the analysis of metabolic networks. In order to refine its predictions, the development of novel methods integrating high-throughput experimental data is currently a key challenge in the field. In this paper, we present a novel set of constraints that integrate tracer-based metabolomics data from Isotope Labeling Experiments and metabolic fluxes in a linear fashion. These constraints are based on Elementary Carbon Modes (ECMs), a recently developed concept that generalizes Elementary Flux Modes at the carbon level. To illustrate the effect of our ECMs-based constraints, a Flux Variability Analysis approach was applied to a previously published metabolic network involving the main pathways in the metabolism of glucose. The addition of our ECMs-based constraints substantially reduced the under-determination resulting from a standard application of Flux Variability Analysis, which shows a clear progress over the state of the art. In addition, our approach is adjusted to deal with combinatorial explosion of ECMs in genome-scale metabolic networks. This extension was applied to infer the maximum biosynthetic capacity of non-essential amino acids in human metabolism. Finally, as linearity is the hallmark of our approach, its importance is discussed at a methodological, computational and theoretical level and illustrated with a practical application in the field of Isotope Labeling Experiments.