CEIT and TECNUN, University of Navarra, 20018 San Sebastian, Spain.
Bioinformatics. 2014 Aug 1;30(15):2197-203. doi: 10.1093/bioinformatics/btu193. Epub 2014 Apr 10.
The concept of Elementary Flux Mode (EFM) has been widely used for the past 20 years. However, its application to genome-scale metabolic networks (GSMNs) is still under development because of methodological limitations. Therefore, novel approaches are demanded to extend the application of EFMs. A novel family of methods based on optimization is emerging that provides us with a subset of EFMs. Because the calculation of the whole set of EFMs goes beyond our capacity, performing a selective search is a proper strategy.
Here, we present a novel mathematical approach calculating EFMs fulfilling additional linear constraints. We validated our approach based on two metabolic networks in which all the EFMs can be obtained. Finally, we analyzed the performance of our methodology in the GSMN of the yeast Saccharomyces cerevisiae by calculating EFMs producing ethanol with a given minimum carbon yield. Overall, this new approach opens new avenues for the calculation of EFMs in GSMNs.
Matlab code is provided in the supplementary online materials
Supplementary data are available at Bioinformatics online.
基本通量模式 (EFM) 的概念在过去 20 年中得到了广泛应用。然而,由于方法上的限制,其在基因组规模代谢网络 (GSMN) 中的应用仍在发展之中。因此,需要新的方法来扩展 EFMs 的应用。一种新的基于优化的方法正在出现,为我们提供了一组 EFM 的子集。由于计算整个 EFM 集超出了我们的能力,因此进行选择性搜索是一种合适的策略。
在这里,我们提出了一种新的数学方法,用于计算满足附加线性约束的 EFM。我们基于两个代谢网络验证了我们的方法,这两个网络中所有的 EFM 都可以得到。最后,我们通过计算产生给定最小碳产率乙醇的 EFM,在酵母酿酒酵母的 GSMN 中分析了我们方法的性能。总的来说,这种新方法为 GSMN 中的 EFM 计算开辟了新的途径。
Matlab 代码在补充在线材料中提供。
补充数据可在“Bioinformatics”在线获取。