Quinn-Bohmann Nick, Carr Alex V, Diener Christian, Gibbons Sean M
Institute for Systems Biology, Seattle, WA, USA.
Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA.
Nat Microbiol. 2025 May;10(5):1055-1066. doi: 10.1038/s41564-025-01972-2. Epub 2025 Apr 11.
Metabolic models of individual microorganisms or small microbial consortia have become standard research tools in the bioengineering and systems biology fields. However, extending metabolic modelling to diverse microbial communities, such as those in the human gut, remains a practical challenge from both modelling and experimental validation perspectives. In complex communities, metabolic models accounting for community dynamics, or those that consider multiple objectives, may provide optimal predictions over simpler steady-state models, but require a much higher computational cost. Here we describe some of the strengths and limitations of microbial community-scale metabolic models and argue for a robust validation framework for developing personalized, mechanistic and accurate predictions of microbial community metabolic behaviours across environmental contexts. Ultimately, quantitatively accurate microbial community-scale metabolic models could aid in the design and testing of personalized prebiotic, probiotic and dietary interventions that optimize for translationally relevant outcomes.
单个微生物或小型微生物群落的代谢模型已成为生物工程和系统生物学领域的标准研究工具。然而，将代谢建模扩展到多样的微生物群落，如人类肠道中的群落，从建模和实验验证的角度来看仍然是一个实际挑战。在复杂群落中，考虑群落动态的代谢模型或考虑多个目标的模型，可能比简单的稳态模型提供更优的预测，但需要高得多的计算成本。在此，我们描述了微生物群落尺度代谢模型的一些优势和局限性，并主张建立一个稳健的验证框架，以开发跨环境背景的个性化、机制性和准确的微生物群落代谢行为预测。最终，定量准确的微生物群落尺度代谢模型有助于设计和测试个性化的益生元、益生菌和饮食干预措施，以优化与转化相关的结果。
