Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria.
Austrian Centre of Industrial Biotechnology, Vienna, Austria.
BMC Bioinformatics. 2021 Nov 10;22(1):547. doi: 10.1186/s12859-021-04417-9.
Elementary flux mode (EFM) analysis is a well-established, yet computationally challenging approach to characterize metabolic networks. Standard algorithms require huge amounts of memory and lack scalability which limits their application to single servers and consequently limits a comprehensive analysis to medium-scale networks. Recently, Avis et al. developed mplrs-a parallel version of the lexicographic reverse search (lrs) algorithm, which, in principle, enables an EFM analysis on high-performance computing environments (Avis and Jordan. mplrs: a scalable parallel vertex/facet enumeration code. arXiv:1511.06487 , 2017). Here we test its applicability for EFM enumeration.
We developed EFMlrs, a Python package that gives users access to the enumeration capabilities of mplrs. EFMlrs uses COBRApy to process metabolic models from sbml files, performs loss-free compressions of the stoichiometric matrix, and generates suitable inputs for mplrs as well as efmtool, providing support not only for our proposed new method for EFM enumeration but also for already established tools. By leveraging COBRApy, EFMlrs also allows the application of additional reaction boundaries and seamlessly integrates into existing workflows.
We show that due to mplrs's properties, the algorithm is perfectly suited for high-performance computing (HPC) and thus offers new possibilities for the unbiased analysis of substantially larger metabolic models via EFM analyses. EFMlrs is an open-source program that comes together with a designated workflow and can be easily installed via pip.
基本通量模式（EFM）分析是一种成熟的、但计算挑战性较大的方法，用于描述代谢网络。标准算法需要大量的内存，并且缺乏可扩展性，这限制了它们在单个服务器上的应用，因此限制了对中等规模网络的全面分析。最近，Avis 等人开发了 mplrs-一种字典序反向搜索（lrs）算法的并行版本，该算法原则上可以在高性能计算环境中进行 EFM 分析（Avis 和 Jordan. mplrs：一种可扩展的并行顶点/面枚举代码。arXiv:1511.06487, 2017）。在这里，我们测试了它在 EFM 枚举中的适用性。
我们开发了 EFMlrs，这是一个 Python 包，为用户提供了对 mplrs 枚举功能的访问。EFMlrs 使用 COBRApy 从 sbml 文件处理代谢模型，对化学计量矩阵进行无损压缩，并为 mplrs 和 efmtool 生成合适的输入，不仅为我们提出的新的 EFM 枚举方法提供支持，还为已建立的工具提供支持。通过利用 COBRApy，EFMlrs 还允许应用额外的反应边界，并无缝集成到现有的工作流程中。
我们表明，由于 mplrs 的特性，该算法非常适合高性能计算（HPC），因此通过 EFM 分析为分析更大规模的代谢模型提供了新的可能性。EFMlrs 是一个开源程序，与指定的工作流程一起提供，并且可以通过 pip 轻松安装。
