School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK.
Department of Biology, University of Padova, Padova, 35122, Veneto, Italy.
Trends Endocrinol Metab. 2024 Jun;35(6):533-548. doi: 10.1016/j.tem.2024.02.018. Epub 2024 Apr 3.
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
基因组规模代谢模型（GEMs）正在通过将生物学数据和知识与数学严谨性相结合，成为研究混合微生物群体的平台。然而，由于可用的计算工具数量不断增加、缺乏通用标准以及它们固有的局限性，将这些模型用于回答研究问题可能具有挑战性。在这里，我们全面介绍了构建和评估微生物群落基因组规模模型的基础概念。然后，我们根据要求、功能和应用对工具进行比较。接下来，我们强调了在采用现有工具和开发新工具时需要考虑的当前陷阱和开放挑战。我们的纲要对于建模者社区的扩展，无论是新手还是有经验的，都具有相关性。
