Reimers Arne C, Bruggeman Frank J, Olivier Brett G, Stougie Leen
1 Department of Mathematics and Computer Science, Freie Universität Berlin , Berlin, Germany .
J Comput Biol. 2015 May;22(5):414-24. doi: 10.1089/cmb.2014.0141. Epub 2015 Jan 7.
Flux balance analysis (FBA) is one of the most often applied methods on genome-scale metabolic networks. Although FBA uniquely determines the optimal yield, the pathway that achieves this is usually not unique. The analysis of the optimal-yield flux space has been an open challenge. Flux variability analysis is only capturing some properties of the flux space, while elementary mode analysis is intractable due to the enormous number of elementary modes. However, it has been found by Kelk et al. (2012) that the space of optimal-yield fluxes decomposes into flux modules. These decompositions allow a much easier but still comprehensive analysis of the optimal-yield flux space. Using the mathematical definition of module introduced by Müller and Bockmayr (2013b), we discovered useful connections to matroid theory, through which efficient algorithms enable us to compute the decomposition into modules in a few seconds for genome-scale networks. Using that every module can be represented by one reaction that represents its function, in this article, we also present a method that uses this decomposition to visualize the interplay of modules. We expect the new method to replace flux variability analysis in the pipelines for metabolic networks.
通量平衡分析（FBA）是基因组规模代谢网络中最常应用的方法之一。尽管FBA能唯一确定最优产量，但实现这一产量的途径通常并非唯一。对最优产量通量空间的分析一直是一个开放性挑战。通量变异性分析仅捕捉了通量空间的一些特性，而由于基本模式数量巨大，基本模式分析难以处理。然而，凯尔克等人（2012年）发现，最优产量通量空间可分解为通量模块。这些分解使得对最优产量通量空间的分析更容易且仍全面。利用米勒和博克迈尔（2013b）引入的模块数学定义，我们发现了与拟阵理论的有用联系，通过这些联系，高效算法能让我们在几秒内为基因组规模网络计算出分解为模块的结果。利用每个模块都可以由一个代表其功能的反应来表示这一点，在本文中，我们还提出了一种利用这种分解来可视化模块相互作用的方法。我们期望这种新方法能在代谢网络流程中取代通量变异性分析。
