Advances in microbial community-scale metabolic models

MA Shuai; ZANG Jiaxi; LIU Shuyue; NIE Yong; WU Xiaolei

doi:10.12211/2096-8280.2026-050

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Advances in microbial community-scale metabolic models

更新时间：2026-07-31

- Advances in microbial community-scale metabolic models
- 微生物群落尺度代谢模型研究进展
- Synthetic Biology Journal Pages: 1-18(2026)
- 作者机构：
  
  1.北京大学，地球与空间科学学院，北京100871
  2.北京大学，力学与工程科学学院，北京100871
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2026-050
  CLC： Q938.1
- Received：02 June 2026，
  
  Revised：2026-07-28，
  
  Online First：31 July 2026，
- 稿件说明：
移动端阅览
马帅，臧嘉熹，刘舒月，聂勇，吴晓磊. 微生物群落尺度代谢模型研究进展［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-050

MA Shuai， ZANG Jiaxi， LIU Shuyue， NIE Yong， WU Xiaolei. Advances in microbial community-scale metabolic models［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-050
马帅，臧嘉熹，刘舒月，聂勇，吴晓磊. 微生物群落尺度代谢模型研究进展［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-050 DOI：

MA Shuai， ZANG Jiaxi， LIU Shuyue， NIE Yong， WU Xiaolei. Advances in microbial community-scale metabolic models［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-050 DOI：

摘要

微生物群落尺度代谢模型（microbial community-scale metabolic model， MCMM）是单菌基因组规模代谢模型（genome-scale metabolic model， GSMM）基础上发展形成的群落代谢建模框架，可用于分析微生物群落中的资源竞争、代谢物交换、交叉供养、功能输出及其对群落构建与演化的影响。本文围绕MCMM的构建基础、主要建模方法、代表性工具及发展方向进行综述。文章首先介绍单菌GSMM的构建流程，包括初始模型构建、修订与结构质量评估和功能验证与优化，阐明单菌模型质量对群落尺度预测结果的影响。随后梳理稳态MCMM的基本思想，比较不同稳态建模方法在群落优化、稳定共存、样本特异性预测和代谢依赖识别等方面的特点，并分析稳态假设在描述群落时间变化和空间异质性方面的局限。在此基础上，介绍动态MCMM的建模思路，通过引入底物消耗、代谢物积累、种群丰度波动等时间变化和空间过程，可描述群落演替、扰动响应和空间异质性。最后，本文讨论MCMM在模型质量、环境约束、参数设定、多组学整合和机器学习辅助建模等方面的发展需求，为微生物群落代谢互作研究和功能预测提供参考。

Abstract

Microbial community-scale metabolic model (MCMM) is a community-level metabolic modelling framework developed on the basis of single-species genome-scale metabolic model (GSMM). By integrating the metabolic networks of multiple community members within a shared environmental context

MCMM provides a mechanistic approach for analysing resource competition

metabolite exchange

cross-feeding

community-level functional outputs

and their potential influences on microbial community assembly and evolution. This review systematically summarizes the construction basis

major modelling approaches

representative computational tools

and future development of MCMM. The general workflow for constructing single-species GSMM includes initial model reconstruction

model refinement

structural quality assessment

functional validation

and further optimization. The reliability of GSMM is affected by genome annotation

reaction completeness

network connectivity

exchange reactions

environmental conditions

and model validation. Because MCMM is generally assembled from multiple individual GSMM

errors or uncertainties in single-species GSMM may accumulate and propagate during community-scale simulation

thereby influencing the prediction of resource allocation

metabolic exchange

and community function. The basic principles of steady-state MCMM are then reviewed. Different steady-state modelling methods are compared in terms of their assumptions

optimization objectives

input requirements

major outputs

and suitable application scenarios. Their characteristics in community-level optimization

stable coexistence prediction

sample-specific metabolic analysis

and identification of metabolic dependency are discussed. Although steady-state MCMM is widely used because of its relatively clear mathematical formulation and manageable computational cost

the steady-state assumption limits its ability to represent temporal variation

population fluctuation

environmental disturbance

and spatial heterogeneity in microbial communities. Dynamic MCMM further extends the modelling framework by incorporating time-dependent substrate consumption

metabolite accumulation

changes in population abundance

environmental feedback

spatial diffusion

and local resource gradients. These processes enable the description of microbial community succession

disturbance response

transient metabolic interaction

and spatially heterogeneous processes. Compared with steady-state MCMM

dynamic MCMM expands community metabolic modelling from the prediction of possible metabolic states to the simulation of ecological and metabolic processes over time and space. The future development of MCMM depends on improvements in model quality

realistic definition of environmental constraints

rational parameter setting

integration of multi-omics data

and machine learning-assisted model reconstruction and prediction. Closer integration between computational modelling and experimental validation is also required to improve the biological reliability

interpretability

and predictive capacity of MCMM. This review provides a reference for mechanistic studies of microbial metabolic interaction and for the prediction of microbial community function.

关键词

Keywords

references

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