Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, 55905, USA; Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, MN, 55905, USA; Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
Metab Eng. 2022 Mar;70:12-22. doi: 10.1016/j.ymben.2021.12.011. Epub 2022 Jan 3.
Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.
用于评估微生物群落的预测建模工具对于实现微生物组在农业、生态学和医学中的变革性能力非常重要。基于约束的群落尺度代谢建模在对微生物群落的结构和功能进行机制预测方面具有独特的潜力。然而,要发挥这种潜力,需要了解关键的物理化学约束条件,这些约束条件通常是按物种为基础来考虑的。目前需要的是一种将与微生物生态学相关的全局约束条件纳入群落模型的方法。资源分配约束描述了有限的资源应该如何分配到不同的细胞过程中,它限制了代谢和生态过程的效率。在这项研究中,我们通过在通量水平上直接对资源分配约束进行简单的、无机理的实现,研究了资源分配约束在群落尺度代谢建模中的意义。通过系统地进行单种、两种和多种物种的生长模拟,我们表明资源分配约束对于预测微生物群落的结构和功能是必不可少的。我们的研究结果呼吁采用一种可扩展的工作流程来实现资源分配约束的机械版本,以最终充分发挥群落尺度代谢建模工具的潜力。