Röhl Annika, Bockmayr Alexander
Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany.
J Math Biol. 2019 Oct;79(5):1749-1777. doi: 10.1007/s00285-019-01409-5. Epub 2019 Aug 6.
Metabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.
代谢网络重建在计算系统生物学中被广泛用于细胞代谢的计算机模拟研究。分析这些模型的一种常用方法是基本通量模式(EFM),它对应于网络中的最小功能单元。对于中等规模的网络,由于其数量巨大,通常不可能计算出所有EFM的集合。从实际角度来看,这可能也没有必要,因为一部分EFM可能就足以回答相关的生物学问题。在本文中,我们研究了MEMo或最小EFM集,它们可以生成代谢网络的所有可能稳态行为。MEMo中的EFM数量可能比EFM总数小几个数量级。使用MEMo,我们可以在代谢网络中计算EFM的生成集,而整个EFM集太大而无法枚举。