Saadat Nima P, van Aalst Marvin, Ebenhöh Oliver
Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
Metabolites. 2022 Mar 22;12(4):275. doi: 10.3390/metabo12040275.
Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, among others, differential-equation-based modeling of metabolic systems, constraint-based modeling and metabolic network expansion of metabolic networks. Most of these methods are well established and are implemented in numerous software packages, but these are scattered between different programming languages, packages and syntaxes. This complicates establishing straight forward pipelines integrating model construction and simulation. We present a Python package moped that serves as an integrative hub for reproducible construction, modification, curation and analysis of metabolic models. moped supports draft reconstruction of models directly from genome/proteome sequences and pathway/genome databases utilizing GPR annotations, providing a completely reproducible model construction and curation process within executable Python scripts. Alternatively, existing models published in SBML format can be easily imported. Models are represented as Python objects, for which a wide spectrum of easy-to-use modification and analysis methods exist. The model structure can be manually altered by adding, removing or modifying reactions, and gap-filling reactions can be found and inspected. This greatly supports the development of draft models, as well as the curation and testing of models. Moreover, moped provides several analysis methods, in particular including the calculation of biosynthetic capacities using metabolic network expansion. The integration with other Python-based tools is facilitated through various model export options. For example, a model can be directly converted into a CobraPy object for constraint-based analyses. moped is a fully documented and expandable Python package. We demonstrate the capability to serve as a hub for integrating reproducible model construction and curation, database import, metabolic network expansion and export for constraint-based analyses.
代谢网络的数学建模是研究代谢和生长基本原理的有力方法。这些方法包括基于微分方程的代谢系统建模、基于约束的建模以及代谢网络的代谢网络扩展等。这些方法大多已成熟，并在众多软件包中得以实现，但它们分散在不同的编程语言、软件包和语法之间。这使得建立整合模型构建和模拟的直接流程变得复杂。我们提出了一个Python软件包moped，它作为一个集成中心，用于代谢模型的可重复构建、修改、管理和分析。moped支持直接从基因组/蛋白质组序列以及利用基因-蛋白质-反应注释的通路/基因组数据库中进行模型的初步重建，在可执行的Python脚本中提供完全可重复的模型构建和管理过程。或者，也可以轻松导入以SBML格式发布的现有模型。模型以Python对象表示，针对这些对象存在广泛的易于使用的修改和分析方法。可以通过添加、删除或修改反应来手动改变模型结构，并且可以找到并检查填补缺口的反应。这极大地支持了初步模型的开发以及模型的管理和测试。此外，moped提供了多种分析方法，特别是包括使用代谢网络扩展来计算生物合成能力。通过各种模型导出选项促进了与其他基于Python的工具的集成。例如，一个模型可以直接转换为CobraPy对象以进行基于约束的分析。moped是一个有完整文档记录且可扩展的Python软件包。我们展示了它作为一个中心的能力，可用于整合可重复的模型构建和管理、数据库导入、代谢网络扩展以及用于基于约束分析的导出。
