从菌株资源库到合成微生物群落：理性设计、工程构建和应用

林慧; 赵晞泽; 蒋荷; 刘双江

doi:10.12211/2096-8280.2026-029

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从菌株资源库到合成微生物群落：理性设计、工程构建和应用

特约评述 | 更新时间：2026-06-26

- 从菌株资源库到合成微生物群落：理性设计、工程构建和应用
- From strain resource biobanks to synthetic microbial communities： rational design， engineered assembly， and applications
- 合成生物学 2026年页码：1-22
- 作者机构：
  
  1.山东大学，微生物改造技术全国重点实验室，山东青岛266237
  2.中国科学院微生物研究所，微生物多样性与资源创新利用全国重点实验室，北京 100101
  3.中国科学院大学，北京 100049
- 作者简介：
  
  林慧（1997—），女，博士。研究方向为微生物互作机制与合成微生物群落的理性构建。
  刘双江（1964—），男，教授，博士生导师，中国科学院微生物研究所研究员、山东大学特聘教授。研究方向为环境微生物学和人体肠道微生物组。
- 基金信息：
  
  国家重点研发计划(2021YFA0909500)
- DOI：10.12211/2096-8280.2026-029
  中图分类号： Q93
- 收稿：2026-04-22，
  
  修回：2026-06-23，
  
  网络首发：2026-06-26，
- 稿件说明：
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林慧，赵晞泽，蒋荷，刘双江. 从菌株资源库到合成微生物群落：理性设计、工程构建和应用［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-029

LIN Hui， ZHAO Xize， JIANG He， LIU Shuangjiang. From strain resource biobanks to synthetic microbial communities： rational design， engineered assembly， and applications［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-029
林慧，赵晞泽，蒋荷，刘双江. 从菌株资源库到合成微生物群落：理性设计、工程构建和应用［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-029 DOI：

LIN Hui， ZHAO Xize， JIANG He， LIU Shuangjiang. From strain resource biobanks to synthetic microbial communities： rational design， engineered assembly， and applications［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-029 DOI：

摘要

自然微生物群落的组成多样性及其互作关系的复杂性、加之受环境与宿主条件动态调控，导致自然形成的微生物群落功能难以实现可预测的定向调控与工程化利用。合成微生物群落（Synthetic Microbial Communities， SynCom）具有明确微生物组成和功能特征，并且按设计规则组装，既可应用于环境污染综合治理、农业增产与医疗健康，也可作为解析生态规律及涌现功能的可控研究体系。随着可培养微生物菌株资源持续增加、菌株资源库不断扩展以及对菌株功能和特性的认识的不断深入，如何利用这些宝贵的微生物菌株资源、在候选菌株集合中定量识别关键互作并完成理性组装，使群落在功能输出与生态稳定性之间达到可预测权衡，已成为合成微生物群落标准化与工程化应用的重要任务和关键挑战。本文提出菌株资源库驱动的互作识别与组装框架：以系统化菌株资源库及标准化元数据为基础，以微生物互作的定量表征为组装规则的核心依据，并融入迭代式设计-构建-测试-学习（DBTL）流程以优化菌群功能—稳定性权衡，支撑个性化应用场景的快速适配。同时，建设菌株、数据与方法的共享基础平台，促进资源互通与定量证据累积，增强跨实验室结果的可比性，并为生物安全评估与规模化转化提供基础支撑。该框架面向污染治理与土壤健康提升等应用需求，推动合成微生物群落从经验试错走向基于定量证据的可预测理性设计。

Abstract

The compositional diversity and complex interaction networks of natural microbial communities

together with their dynamic regulation by environmental and host conditions

make it difficult to achieve predictable

targeted control and engineered utilization of naturally formed microbiomes. Synthetic microbial communities

also known as SynComs

are assembled according to defined design principles and possess clearly characterized microbial compositions and functional traits. They can be applied to integrated environmental pollution remediation and agricultural productivity enhancement

and can also serve as controllable experimental systems for elucidating ecological rules and emergent functions. With the continuous increase in culturable microbial strain resources

the expansion of strain resource banks

and the deepening understanding of strain functions and traits

a central task and key challenge for the standardization and engineering application of synthetic microbial communities is how to effectively utilize these valuable microbial strain resources. Specifically

it is necessary to quantitatively identify key interactions within candidate strain sets and achieve rational assembly

so that community-level functional outputs and ecological stability can be predictably balanced. Here

we propose a strain-resource-bank-driven framework for identifying interactions and assembling communities. This framework is based on systematic strain resource banks and standardized metadata

takes quantitative characterization of microbial interactions as the core basis for assembly rules

and incorporates an iterative design-build-test-learn

or DBTL

workflow to optimize the trade-off between community function and stability. In this way

it supports rapid adaptation to personalized application scenarios. Meanwhile

the establishment of shared infrastructure platforms integrating strains

data

and methods will promote resource interoperability and the accumulation of quantitative evidence

enhance the comparability of results across laboratories

and provide foundational support for biosafety assessment and large-scale translation. Oriented toward application needs such as pollution remediation and soil health improvement

this framework aims to advance synthetic microbial communities from empirical trial-and-error approaches toward predictable

rational design based on quantitative evidence.

关键词

Keywords

references

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