Systems Biotechnology and Biorefining, National Food Institute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark and Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Bioinformatics. 2014 Nov 15;30(22):3232-9. doi: 10.1093/bioinformatics/btu529. Epub 2014 Aug 6.
Elementary flux mode (EFM) is a useful tool in constraint-based modeling of metabolic networks. The property that every flux distribution can be decomposed as a weighted sum of EFMs allows certain applications of EFMs to studying flux distributions. The existence of biologically infeasible EFMs and the non-uniqueness of the decomposition, however, undermine the applicability of such methods. Efforts have been made to find biologically feasible EFMs by incorporating information from transcriptional regulation and thermodynamics. Yet, no attempt has been made to distinguish biologically feasible EFMs by considering their graphical properties. A previous study on the transcriptional regulation of metabolic genes found that distinct branches at a branch point metabolite usually belong to distinct metabolic pathways. This suggests an intuitive property of biologically feasible EFMs, i.e. minimal branching.
We developed the concept of minimal branching EFM and derived the minimal branching decomposition (MBD) to decompose flux distributions. Testing in the core Escherichia coli metabolic network indicated that MBD can distinguish branches at branch points and greatly reduced the solution space in which the decomposition is often unique. An experimental flux distribution from a previous study on mouse cardiomyocyte was decomposed using MBD. Comparison with decomposition by a minimum number of EFMs showed that MBD found EFMs more consistent with established biological knowledge, which facilitates interpretation. Comparison of the methods applied to a complex flux distribution in Lactococcus lactis similarly showed the advantages of MBD. The minimal branching EFM concept underlying MBD should be useful in other applications.
sinhu@bio.dtu.dk or p.ji@polyu.edu.hk
Supplementary data are available at Bioinformatics online.
基本通量模式(EFM)是代谢网络约束建模的有用工具。通量分布可以分解为 EFM 的加权和的性质允许 EFM 在研究通量分布方面的某些应用。然而,生物不可行的 EFM 的存在和分解的非唯一性破坏了这些方法的适用性。已经通过整合转录调节和热力学的信息来寻找生物可行的 EFM。然而,还没有人试图通过考虑它们的图形特性来区分生物可行的 EFM。先前关于代谢基因转录调节的研究发现,分支点代谢物的不同分支通常属于不同的代谢途径。这表明了生物可行的 EFM 的一个直观特性,即最小分支。
我们提出了最小分支 EFM 的概念,并推导出最小分支分解(MBD)来分解通量分布。在核心大肠杆菌代谢网络中的测试表明,MBD 可以区分分支点的分支,并且大大减少了分解通常唯一的解空间。使用 MBD 对先前在鼠心肌细胞上进行的一项实验通量分布进行了分解。与通过最小数量的 EFM 进行的分解进行比较表明,MBD 发现的 EFM 与已建立的生物学知识更一致,这有助于解释。对乳球菌乳糖中复杂通量分布应用的方法进行比较同样显示了 MBD 的优势。MBD 所基于的最小分支 EFM 概念在其他应用中应该是有用的。
sinhu@bio.dtu.dk 或 p.ji@polyu.edu.hk
补充数据可在 Bioinformatics 在线获得。