Faust Karoline, Croes Didier, van Helden Jacques
Research Group of Bioinformatics and (Eco-)Systems Biology (BSB), VIB - Vrije Universiteit Brussel, Pleinlaan, Belgium.
Biosystems. 2011 Aug;105(2):109-21. doi: 10.1016/j.biosystems.2011.05.004. Epub 2011 May 27.
The analysis of a variety of data sets (transcriptome arrays, phylogenetic profiles, etc.) yields groups of functionally related genes. In order to determine their biological function, associated gene groups are often projected onto known pathways or tested for enrichment of known functions. However, these approaches are not flexible enough to deal with variations or novel pathways. During the last decade, we developed and refined an approach that predicts metabolic pathways from a global metabolic network encompassing all known reactions and their substrates/products, by extracting a subgraph connecting at best a set of seed nodes (compounds, reactions, enzymes or enzyme-coding genes). In this review, we summarize this work, while discussing the problems and pitfalls but also the advantages and applications of network-based metabolic pathway prediction.
对各种数据集（转录组阵列、系统发育谱等）的分析产生了功能相关的基因群。为了确定它们的生物学功能，相关的基因群常常被投射到已知的途径上，或者进行已知功能富集的测试。然而，这些方法在处理变异或新途径时不够灵活。在过去十年中，我们开发并完善了一种方法，该方法通过提取一个最好连接一组种子节点（化合物、反应、酶或酶编码基因）的子图，从包含所有已知反应及其底物/产物的全局代谢网络预测代谢途径。在这篇综述中，我们总结了这项工作，同时讨论了基于网络的代谢途径预测的问题和陷阱，以及优点和应用。
