From strain resource biobanks to synthetic microbial communities： rational design， engineered assembly， and applications

LIN Hui; ZHAO Xize; JIANG He; LIU Shuangjiang

doi:10.12211/2096-8280.2026-029

您当前的位置：

首页 >

文章列表页 >

From strain resource biobanks to synthetic microbial communities： rational design， engineered assembly， and applications

更新时间：2026-06-26

- From strain resource biobanks to synthetic microbial communities： rational design， engineered assembly， and applications
- Synthetic Biology Journal Pages: 1-22(2026)
- 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：
- 基金信息：
- DOI：10.12211/2096-8280.2026-029
  CLC： Q93
- Received：22 April 2026，
  
  Revised：2026-06-23，
  
  Online First：26 June 2026，
- 稿件说明：
移动端阅览
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

LAWSON C E , HARCOMBE W R , HATZENPICHLER R , et al . Common principles and best practices for engineering microbiomes [J ] . Nature Reviews Microbiology , 2019 , 17 ( 12 ): 725 - 41 .

TRIVEDI P , LEACH J E , TRINGE S G , et al . Plant-microbiome interactions: from community assembly to plant health [J ] . Nature Reviews Microbiology , 2020 , 18 ( 11 ): 607 - 21 .

VAN DEN BERG N I , MACHADO D , SANTOS S , et al . Ecological modelling approaches for predicting emergent properties in microbial communities [J ] . Nature Ecology & Evolution , 2022 , 6 ( 7 ): 855 - 65 .

HU J , AMOR D R , BARBIER M , et al . Emergent phases of ecological diversity and dynamics mapped in microcosms [J ] . Science , 2022 , 378 ( 6615 ): 85 - 9 .

OÑA L , SHREEKAR S K , KOST C . Disentangling microbial interaction networks [J ] . Trends in Microbiology , 2025 , 33 ( 6 ): 619 - 34 .

GELLER A M , LEVY A . " What I cannot create, I do not understand": elucidating microbe-microbe interactions to facilitate plant microbiome engineering [J ] . Current Opinion in Microbiology , 2023 , 72 ( 102283 ).

FREILICH S , ZARECKI R , EILAM O , et al . Competitive and cooperative metabolic interactions in bacterial communities [J ] . Nature Communications , 2011 , 2 ( 1 ): 589 .

AUCHTUNG J M , HALLEN-ADAMS H E , HUTKINS R . Microbial interactions and ecology in fermented food ecosystems [J ] . Nature Reviews Microbiology , 2025 , 23 ( 10 ): 622 - 34 .

VORHOLT J A , VOGEL C , CARLSTRÖM C I , et al . Establishing causality: opportunities of synthetic communities for plant microbiome research [J ] . Cell Host & Microbe , 2017 , 22 ( 2 ): 142 - 55 .

JOHNS N I , BLAZEJEWSKI T , GOMES A L , et al . Principles for designing synthetic microbial communities [J ] . Current Opinion in Microbiology , 2016 , 31 ( 146-53 ).

GROSSKOP T , SOYER O S . Synthetic microbial communities [J ] . Current Opinion in Microbiology , 2014 , 18(72 - 7 .

MCCARTY N S , LEDESMA-AMARO R . Synthetic biology tools to engineer microbial communities for biotechnology [J ] . Trends in Biotechnology , 2019 , 37 ( 2 ): 181 - 97 .

ZOMORRODI A R , SEGRÈ D . Synthetic ecology of microbes: mathematical models and applications [J ] . Journal of Molecular Biology , 2016 , 428 ( 5 ): 837 - 61 .

DAL BELLO M , LEE H , GOYAL A , et al . Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism [J ] . Nature Ecology & Evolution , 2021 , 5 ( 10 ): 1424 - 34 .

LEE H , BLOXHAM B , GORE J . Resource competition can explain simplicity in microbial community assembly [J ] . Proceedings of the National Academy of Sciences , 2023 , 120 ( 35 ): e2212113120 .

HU J , BARBIER M , BUNIN G , et al . Collective dynamical regimes predict invasion success and impacts in microbial communities [J ] . Nature Ecology & Evolution , 2025 , 9 ( 3 ): 406 - 16 .

MEHLFERBER E C , ARNAULT G , JOSHI B , et al . A cross-systems primer for synthetic microbial communities [J ] . Nature Microbiology , 2024 , 9 ( 11 ): 2765 - 73 .

RYLOTT E L , BRUCE N C . How synthetic biology can help bioremediation [J ] . Current Opinion in Chemical Biology , 2020 , 58 ( 86-95 ).

CHEN C , LI K , LIN X , et al . Engineering natural microbial communities: harnessing synthetic communities for bioremediation [J ] . Current Opinion in Microbiology , 2025 , 87 ( 102652 ).

CUI Y , TAO Y , LUO Y , et al . Harnessing synthetic microbial communities for sustainable agriculture: Enhancing soil health and crop yields [J ] . Critical Reviews in Environmental Science and Technology , 2025 , 55 ( 19 ): 1479 - 505 .

XU X , DINESEN C , PIOPPI A , et al . Composing a microbial symphony: synthetic communities for promoting plant growth [J ] . Trends in Microbiology , 2025 ,

KIM M S , BISANZ J E . Design and application of synthetic human gut microbial communities [J ] . Gut Microbes , 2025 , 17 ( 1 ): 2575923 .

VAN LEEUWEN P T , BRUL S , ZHANG J , et al . Synthetic microbial communities (SynComs) of the human gut: design, assembly, and applications [J ] . FEMS Microbiology Reviews , 2023 , 47 ( 2 ): fuad012 .

ZHANG Y , JING M , LYU L , et al . Principles for rigorous design and application of synthetic microbial communities [J ] . Advanced Science , 2026 , 13 ( 10 ): e14750 .

JING J , GARBEVA P , RAAIJMAKERS J M , et al . Strategies for tailoring functional microbial synthetic communities [J ] . The ISME Journal , 2024 , 18 ( 1 ): wrae049 .

FANG X , LLOYD C J , PALSSON B O . Reconstructing organisms in silico: genome-scale models and their emerging applications [J ] . Nature Reviews Microbiology , 2020 , 18 ( 12 ): 731 - 43 .

GRIMM V , RAILSBACK S F . Individual-based modeling and ecology [M ] . Individual-based modeling and ecology . Princeton university press . 2013 .

GARCÉS-RUIZ M , DÍAZ-OTERO B G , ANTONIELLI L , et al . Machine learning for designing low-risk microbial consortia pesticides [J ] . Trends in Biotechnology , 2025 .

PHAM V H , KIM J . Cultivation of unculturable soil bacteria [J ] . Trends in Biotechnology , 2012 , 30 ( 9 ): 475 - 84 .

BERDY B , SPOERING A L , LING L L , et al . In situ cultivation of previously uncultivable microorganisms using the ichip [J ] . Nature Protocols , 2017 , 12 ( 10 ): 2232 - 42 .

NICHOLS D , CAHOON N , TRAKHTENBERG E , et al . Use of ichip for high-throughput in situ cultivation of “uncultivable” microbial species [J ] . Applied and Environmental Microbiology , 2010 , 76 ( 8 ): 2445 - 50 .

LAGIER J-C , KHELAIFIA S , ALOU M T , et al . Culture of previously uncultured members of the human gut microbiota by culturomics [J ] . Nature Microbiology , 2016 , 1 ( 12 ).

LEWIS W H , TAHON G , GEESINK P , et al . Innovations to culturing the uncultured microbial majority [J ] . Nature Reviews Microbiology , 2021 , 19 ( 4 ): 225 - 40 .

SCHOBER I , KOBLITZ J , SARDÀ CARBASSE J , et al . Bac Dive in 2025: the core database for prokaryotic strain data [J ] . Nucleic Acids Research , 2025 , 53 ( D1 ): D748 - D56 .

FREESE H M , MEIER-KOLTHOFF J P , SARDÀ CARBASSE J , et al . TYGS and LPSN in 2025: a Global Core Biodata Resource for genome-based classification and nomenclature of prokaryotes within DSMZ Digital Diversity [J ] . Nucleic Acids Research , 2026 , 54 ( D1 ): D884 - D91 .

PARKS D H , CHAUMEIL P-A , MUSSIG A J , et al . GTDB release 10: a complete and systematic taxonomy for 715 230 bacterial and 17 245 archaeal genomes [J ] . Nucleic Acids Research , 2026 , 54 ( D1 ): D743 - D54 .

CHEN I-M A , CHU K , PALANIAPPAN K , et al . The IMG/M data management and analysis system v. 7: content updates and new features [J ] . Nucleic Acids Research , 2023 , 51 ( D1 ): D723 - D32 .

ARKIN A P , COTTINGHAM R W , HENRY C S , et al . KBase: the United States department of energy systems biology knowledgebase [J ] . Nature Biotechnology , 2018 , 36 ( 7 ): 566 - 9 .

RICHARDSON L , ALLEN B , BALDI G , et al . MGnify: the microbiome sequence data analysis resource in 2023 [J ] . Nucleic Acids Research , 2023 , 51 ( D1 ): D753 - D9 .

LOCEY K J , LENNON J T . Scaling laws predict global microbial diversity [J ] . Proceedings of the National Academy of Sciences , 2016 , 113 ( 21 ): 5970 - 5 .

ZHANG J , LIU Y-X , GUO X , et al . High-throughput cultivation and identification of bacteria from the plant root microbiota [J ] . Nature Protocols , 2021 , 16 ( 2 ): 988 - 1012 .

SALCHER M M , LAYOUN P , FERNANDES C , et al . Bringing the uncultivated microbial majority of freshwater ecosystems into culture [J ] . Nature Communications , 2025 , 16 ( 1 ): 7971 .

ZHANG J , LIANG Q Y , MU D S , et al . Cultivating the uncultured: Harnessing the “sandwich agar plate” approach to isolate heme‐dependent bacteria from marine sediment [J ] . Mlife , 2024 , 3 ( 1 ): 143 - 55 .

LIU C , ZHOU N , DU M-X , et al . The Mouse Gut Microbial Biobank expands the coverage of cultured bacteria [J ] . Nature Communications , 2020 , 11 ( 1 ): 79 .

LI D , LIU C , ABUDUAINI R , et al . The monkey microbial biobank brings previously uncultivated bioresources for nonhuman primate and human gut microbiomes [J ] . Mlife , 2022 , 1 ( 2 ): 210 - 7 .

LIU C , DU M-X , ABUDUAINI R , et al . Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank [J ] . Microbiome , 2021 , 9 ( 1 ): 119 .

ABDUGHENI R , WANG W Z , WANG Y J , et al . Metabolite profiling of human‐originated Lachnospiraceae at the strain level [J ] . Imeta , 2022 , 1 ( 4 ): e58 .

SUN X-W , HUANG H-J , WANG X-M , et al . Christensenella strain resources, genomic/metabolomic profiling, and association with host at species level [J ] . Gut Microbes , 2024 , 16 ( 1 ): 2347725 .

NIU B , PAULSON J N , ZHENG X , et al . Simplified and representative bacterial community of maize roots [J ] . Proceedings of the National Academy of Sciences , 2017 , 114 ( 12 ): E2450 - E9 .

WINTERMUTE E H , SILVER P A . Emergent cooperation in microbial metabolism [J ] . Molecular systems biology , 2010 , 6407 .

MEE M T , COLLINS J J , CHURCH G M , et al . Syntrophic exchange in synthetic microbial communities [J ] . Proceedings of the National Academy of Sciences , 2014 , 111 ( 20 ): E2149 - E56 .

PENG H , DARLINGTON A P , SOUTH E J , et al . A molecular toolkit of cross-feeding strains for engineering synthetic yeast communities [J ] . Nature Microbiology , 2024 , 9 ( 3 ): 848 - 63 .

ZHAO M , ZHENG G , KANG X , et al . Arsenic pollution remediation mechanism and preliminary application of arsenic-oxidizing bacteria isolated from industrial wastewater [J ] . Environmental Pollution , 2023 , 324 ( 121384 ).

PÁPAI M , BENEDEK T , TÁNCSICS A , et al . Selective enrichment, identification, and isolation of diclofenac, ibuprofen, and carbamazepine degrading bacteria from a groundwater biofilm [J ] . Environmental Science and Pollution Research , 2023 , 30 ( 15 ): 44518 - 35 .

BALDANI J I , REIS V M , VIDEIRA S S , et al . The art of isolating nitrogen-fixing bacteria from non-leguminous plants using N-free semi-solid media: a practical guide for microbiologists [J ] . Plant and Soil , 2014 , 384 ( 1 ): 413 - 31 .

DE ZUTTER N , AMEYE M , VERMEIR P , et al . Innovative rhizosphere-based enrichment under P-limitation selects for bacterial isolates with high-performance P-solubilizing traits [J ] . Microbiology Spectrum , 2022 , 10 ( 6 ): e02052-22 .

SHARMA A , LEE H-J . Antimicrobial activity of probiotic bacteria isolated from plants: A review [J ] . Foods , 2025 , 14 ( 3 ): 495 .

XUN W , REN Y , YAN H , et al . Sustained inhibition of maize seed‐borne Fusarium using a Bacillus‐dominated rhizospheric stable core microbiota with unique cooperative patterns [J ] . Advanced Science , 2023 , 10 ( 5 ): 2205215 .

QIAO Y , WANG Z , SUN H , et al . Synthetic community derived from grafted watermelon rhizosphere provides protection for ungrafted watermelon against Fusarium oxysporum via microbial synergistic effects [J ] . Microbiome , 2024 , 12 ( 1 ): 101 .

ZHOU X , WANG J , LIU F , et al . Cross-kingdom synthetic microbiota supports tomato suppression of Fusarium wilt disease [J ] . Nature Communications , 2022 , 13 ( 1 ): 7890 .

SARNAIK A , LIU A , NIELSEN D , et al . High-throughput screening for efficient microbial biotechnology [J ] . Current Opinion in Biotechnology , 2020 , 64 ( 141-50 ).

LIU X , PAINTER R , ENESA K , et al . High-throughput screening of antibiotic-resistant bacteria in picodroplets [J ] . Lab on a Chip , 2016 , 16 ( 9 ): 1636 - 43 .

HAZAN R , QUE Y-A , MAURA D , et al . A method for high throughput determination of viable bacteria cell counts in 96-well plates [J ] . BMC microbiology , 2012 , 12 ( 1 ): 259 .

HARCOMBE W . Novel cooperation experimentally evolved between species [J ] . Evolution , 2010 , 64 ( 7 ): 2166 - 72 .

RUGBJERG P , SARUP-LYTZEN K , NAGY M , et al . Synthetic addiction extends the productive life time of engineered Escherichia coli populations [J ] . Proceedings of the National Academy of Sciences , 2018 , 115 ( 10 ): 2347 - 52 .

NORTHEN T R , KLEINER M , TORRES M , et al . Community standards and future opportunities for synthetic communities in plant-microbiota research [J ] . Nature Microbiology , 2024 , 9 ( 11 ): 2774 - 84 .

SASSE J , KANT J , COLE B J , et al . Multilab EcoFAB study shows highly reproducible physiology and depletion of soil metabolites by a model grass [J ] . New Phytologist , 2019 , 222 ( 2 ): 1149 - 60 .

LISSIN A , SCHOBER I , WITTE J F , et al . StrainInfo—the central database for linked microbial strain identifiers [J ] . Database , 2025 , 2025(baaf059).

MEROZ N , LIVNY T , FRIEDMAN J . Quantifying microbial interactions: concepts, caveats, and applications [J ] . Current Opinion in Microbiology , 2024 , 80 ( 102511 ).

DI MARTINO R , PICOT A , MITRI S . Oxidative stress changes interactions between 2 bacterial species from competitive to facilitative [J ] . PLoS Biology , 2024 , 22 ( 2 ): e3002482 .

KEHE J , ORTIZ A , KULESA A , et al . Positive interactions are common among culturable bacteria [J ] . Science Advances , 2021 , 7 ( 45 ): eabi7159 .

BAICHMAN-KASS A , SONG T , FRIEDMAN J . Competitive interactions between culturable bacteria are highly non-additive [J ] . Elife , 2023 , 12(e83398).

CHANG C-Y , BAJIĆ D , VILA J C , et al . Emergent coexistence in multispecies microbial communities [J ] . Science , 2023 , 381 ( 6655 ): 343 - 8 .

DAL CO A , VAN VLIET S , KIVIET D J , et al . Short-range interactions govern the dynamics and functions of microbial communities [J ] . Nature Ecology & Evolution , 2020 , 4 ( 3 ): 366 - 75 .

VAN TATENHOVE-PEL R J , RIJAVEC T , LAPANJE A , et al . Microbial competition reduces metabolic interaction distances to the low µm-range [J ] . The ISME Journal , 2021 , 15 ( 3 ): 688 - 701 .

WANG M , CHEN X , MA Y , et al . Type IV pilus shapes a 'bubble-burst'pattern opposing spatial intermixing of two interacting bacterial populations [J ] . Microbiology Spectrum , 2022 , 10 ( 1 ): e01944-21 .

CHEN X , WANG M , LUO L , et al . Substrate toxicity drives successive range expansions opposing spatial intermixing in cross-feeding consortia [J ] . ISME Communications , 2026 , 6 ( 1 ):

VENTURELLI O S , CARR A C , FISHER G , et al . Deciphering microbial interactions in synthetic human gut microbiome communities [J ] . Mol Syst Biol , 2018 , 14 ( 6 ): e8157 .

GENCEL M , COFINO G M , HUI C , et al . Quantifying the intra-and inter-species community interactions in microbiomes by dynamic covariance mapping [J ] . Nature Communications , 2025 , 16 ( 1 ): 6314 .

GOLDFORD J E , LU N , BAJIĆ D , et al . Emergent simplicity in microbial community assembly [J ] . Science , 2018 , 361 ( 6401 ): 469 - 74 .

LEE K K , LIU S , CROCKER K , et al . Functional regimes define soil microbiome response to environmental change [J ] . Nature , 2025 , 644 ( 8078 ): 1028 - 38 .

CORRAL LÓPEZ R , BONACHELA J A , DOMINGUEZ-BELLO M G , et al . Imbalance in gut microbial interactions as a marker of health and disease [J ] . Science , 2026 , 391 ( 6788 ): 890 - 5 .

ZOMORRODI A R , MARANAS C D . OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities [J ] . PLOS Computational Biology , 2012 , 8 ( 2 ): e1002363 .

CHAN S H J , SIMONS M N , MARANAS C D . SteadyCom: predicting microbial abundances while ensuring community stability [J ] . PLOS Computational Biology , 2017 , 13 ( 5 ): e1005539 .

ZELEZNIAK A , ANDREJEV S , PONOMAROVA O , et al . Metabolic dependencies drive species co-occurrence in diverse microbial communities [J ] . Proceedings of the National Academy of Sciences , 2015 , 112 ( 20 ): 6449 - 54 .

DIENER C , GIBBONS S M , RESENDIS-ANTONIO O . MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota [J ] . MSystems , 2020 , 5 ( 1 ): 10 .1128/msystems. 00606 - 19 .

DUKOVSKI I , BAJIĆ D , CHACÓN J M , et al . A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS) [J ] . Nature Protocols , 2021 , 16 ( 11 ): 5030 - 82 .

GARCÍA-JIMÉNEZ B , GARCÍA J L , NOGALES J . FLYCOP: metabolic modeling-based analysis and engineering microbial communities [J ] . Bioinformatics , 2018 , 34 ( 17 ): i954 - i63 .

SCHÄFER M , PACHECO A R , KÜNZLER R , et al . Metabolic interaction models recapitulate leaf microbiota ecology [J ] . Science , 2023 , 381 ( 6653 ): eadf5121 .

PENG Q , ZHAO C , WANG X , et al . Modeling bacterial interactions uncovers the importance of outliers in the coastal lignin-degrading consortium [J ] . Nature Communications , 2025 , 16 ( 1 ): 639 .

HELLWEGER F L , CLEGG R J , CLARK J R , et al . Advancing microbial sciences by individual-based modelling [J ] . Nature Reviews Microbiology , 2016 , 14 ( 7 ): 461 - 71 .

GRANATO E T , SMITH W P , FOSTER K R . Collective protection against the type VI secretion system in bacteria [J ] . The ISME Journal , 2023 , 17 ( 7 ): 1052 - 62 .

WANG J , HASHEM I , BHONSALE S , et al . Individual-based modeling unravels spatial and social interactions in bacterial communities [J ] . The ISME Journal , 2025 , 19 ( 1 ): wraf116 .

BAUER E , ZIMMERMANN J , BALDINI F , et al . BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities [J ] . PLOS Computational Biology , 2017 , 13 ( 5 ): e1005544 .

DAMA A C , KIM K S , LEYVA D M , et al . BacterAI maps microbial metabolism without prior knowledge [J ] . Nature Microbiology , 2023 , 8 ( 6 ): 1018 - 25 .

EMMENEGGER B , MASSONI J , PESTALOZZI C M , et al . Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning [J ] . Nature Communications , 2023 , 14 ( 1 ): 7983 .

DE LA VEGA-CAMARILLO E , ARREOLA-VARGAS J , ANTONY-BABU S , et al . Machine learning-guided synthetic microbial communities enable functional and sustainable degradation of persistent environmental pollutants [J ] . Environmental Science & Technology , 2025 .

FRIEDMAN J , HIGGINS L M , GORE J . Community structure follows simple assembly rules in microbial microcosms [J ] . Nature Ecology Evolution , 2017 , 1 ( 5 ): 109 .

WANG W , XIA Y , ZHANG P , et al . Narrow-spectrum resource-utilizing bacteria drive the stability of synthetic communities through enhancing metabolic interactions [J ] . Nature Communications , 2025 , 16 ( 1 ): 6088 .

WANG M , CHEN X , FANG Y , et al . The trade-off between individual metabolic specialization and versatility determines the metabolic efficiency of microbial communities [J ] . Cell Systems , 2024 , 15 ( 1 ): 63 - 74.e5 .

PONTRELLI S , SZABO R , POLLAK S , et al . Metabolic cross-feeding structures the assembly of polysaccharide degrading communities [J ] . Science Advances , 2022 , 8 ( 8 ): eabk3076 .

MCCLURE R , FARRIS Y , DANCZAK R , et al . Interaction networks are driven by community-responsive phenotypes in a chitin-degrading consortium of soil microbes [J ] . MSystems , 2022 , 7 ( 5 ): e00372-22 .

SMITH P , SCHUSTER M . Public goods and cheating in microbes [J ] . Current Biology , 2019 , 29 ( 11 ): R442 - R7 .

DUNNY G M , BRICKMAN T J , DWORKIN M . Multicellular behavior in bacteria: communication, cooperation, competition and cheating [J ] . Bioessays , 2008 , 30 ( 4 ): 296 - 8 .

PANDE S , KAFTAN F , LANG S , et al . Privatization of cooperative benefits stabilizes mutualistic cross-feeding interactions in spatially structured environments [J ] . The ISME Journal , 2016 , 10 ( 6 ): 1413 - 23 .

ROSS T D , IM H , KEOGH B G , et al . Metabolic interplay drives population cycles in a cross-feeding microbial community [J ] . Nature Communications , 2025 , 16 ( 1 ): 8919 .

LIN H , WANG D , WANG Q , et al . Interspecific competition prevents the proliferation of social cheaters in an unstructured environment [J ] . The ISME Journal , 2024 , 18 ( 1 ): wrad038 .

LI Y , LI R , LIU R , et al . A simplified SynCom based on core-helper strain interactions enhances symbiotic nitrogen fixation in soybean [J ] . J Integr Plant Biol , 2025 , 67 ( 6 ): 1582 - 98 .

DUBEY S , BHATTACHARJEE A , OZA Y , et al . Harnessing SynComs for rhizosphere engineering to alleviate salt stress in Vigna radiata: from lab experiments to the field [J ] . Plant Physiology and Biochemistry , 2025 , 110304 .

ZHAO W , SHI L , HAN Y , et al . Development of a microbiome for phenolic metabolism based on a domestication approach from lab to industrial application [J ] . Communications Biology , 2024 , 7 ( 1 ): 1716 .

PARK Y-K , PENG H , HAPETA P , et al . Engineered cross-feeding creates inter-and intra-species synthetic yeast communities with enhanced bioproduction [J ] . Nature Communications , 2024 , 15 ( 1 ): 8924 .

SHIN J , LIAO S , KUANYSHEV N , et al . Compositional and temporal division of labor modulates mixed sugar fermentation by an engineered yeast consortium [J ] . Nature Communications , 2024 , 15 ( 1 ): 781 .

KANG N K , KOH H G , CHOI Y , et al . Bioconversion of CO2 into valuable bioproducts via synthetic modular co-culture of engineered Chlamydomonas reinhardtii and Escherichia coli [J ] . Metabolic Engineering , 2025 , 90 ( 57-66 ).

RUAN Z , CHEN K , CAO W , et al . Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling [J ] . Nature Communications , 2024 , 15 ( 1 ): 4694 .

WU T , GUO S-Z , ZHANG Y , et al . The engineering of TBBPA-degrading synthetic microbiomes with integrated strategies [J ] . npj Biofilms and Microbiomes , 2025 , 11 ( 1 ): 139 .

JUANG D S , WIGHTMAN W E , LOZANO G L , et al . Microbial community interactions on a chip [J ] . Proceedings of the National Academy of Sciences , 2024 , 121 ( 39 ): e2403510121 .

KEHE J , KULESA A , ORTIZ A , et al . Massively parallel screening of synthetic microbial communities [J ] . Proceedings of the National Academy of Sciences , 2019 , 116 ( 26 ): 12804 - 9 .

KRISHNA KUMAR R , FOSTER K R . 3D printing of microbial communities: A new platform for understanding and engineering microbiomes [J ] . Microbial Biotechnology , 2023 , 16 ( 3 ): 489 - 93 .

WU T , ZHAO X-Z , GUO W-H , et al . Spatial assembly of functional microbiomes via 3D bioprinting for modular remediation of mixed organic pollutants [J ] . Environmental Technology & Innovation , 2026 , 42 ( 104934 ).

WANG L , ZHANG X , TANG C , et al . Engineering consortia by polymeric microbial swarmbots [J ] . Nature Communications , 2022 , 13 ( 1 ): 3879 .

FALLER L , LEITE M F , KURAMAE E E . Enhancing phosphate-solubilising microbial communities through artificial selection [J ] . Nature Communications , 2024 , 15 ( 1 ): 1649 .

ARIAS-SÁNCHEZ F I , VESSMAN B , HAYM A , et al . Artificial selection improves pollutant degradation by bacterial communities [J ] . Nature Communications , 2024 , 15 ( 1 ): 7836 .

LI E , DE JONGE R , LIU C , et al . Rapid evolution of bacterial mutualism in the plant rhizosphere [J ] . Nature Communications , 2021 , 12 ( 1 ): 3829 .

HENRY L P , BERGELSON J . Applying ecological principles to microbiome engineering [J ] . Nature Microbiology , 2025 , 10 ( 9 ): 2111 - 21 .

VAN ELSAS J D , CHIURAZZI M , MALLON C A , et al . Microbial diversity determines the invasion of soil by a bacterial pathogen [J ] . Proceedings of the National Academy of Sciences , 2012 , 109 ( 4 ): 1159 - 64 .

MALLON C A , VAN ELSAS J D , SALLES J F . Microbial invasions: the process, patterns, and mechanisms [J ] . Trends in Microbiology , 2015 , 23 ( 11 ): 719 - 29 .

CARLSTRÖM C I , FIELD C M , BORTFELD-MILLER M , et al . Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere [J ] . Nature Ecology & Evolution , 2019 , 3 ( 10 ): 1445 - 54 .

HE J , ZHANG L , VAN DINGENEN J , et al . Arbuscular mycorrhizal hyphae facilitate rhizobia dispersal and nodulation in legumes [J ] . The ISME Journal , 2024 , 18 ( 1 ): wrae185 .

ZHALNINA K , LOUIE K B , HAO Z , et al . Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly [J ] . Nature Microbiology , 2018 , 3 ( 4 ): 470 - 80 .

HU L , ROBERT C A , CADOT S , et al . Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota [J ] . Nature Communications , 2018 , 9 ( 1 ): 2738 .

FONSECA-GARCÍA C , PETTINGA D , WILSON A , et al . Defined synthetic microbial communities colonize and benefit field-grown sorghum [J ] . The ISME Journal , 2024 , 18 ( 1 ): wrae126 .

TRAN K M , NONG N T , REN J , et al . Genetic "expiry-date" circuits control lifespan of synthetic scavenger bacteria for safe bioremediation [J ] . Nucleic Acids Research , 2025 , 53 ( 14 ).

HAO X , WANG X , CHEN C , et al . Synthetic bacterial communities reshape microbial communities and enhance nutrient supply in desertified land of Northwest China [J ] . Applied Soil Ecology , 2023 , 189 ( 104972 ).

CUI K , XU Q , SHENG X , et al . The Impact of Bioaugmentation on the Performance and Microbial Community Dynamics of an Industrial-Scale Activated Sludge Sequencing Batch Reactor under Various Loading Shocks of Heavy Oil Refinery Wastewater [J ] . Water , 2021 , 13 ( 20 ): 2822 .

ZHOU X , SUN Y , WANG T , et al . Remediation potential of an immobilized microbial consortium with corn straw as a carrier in polycyclic aromatic hydrocarbons contaminated soil [J ] . Journal of Hazardous Materials , 2024 , 469 ( 134091 ).

TARIQ A , GUO S , FARHAT F , et al . Engineering synthetic microbial communities: diversity and applications in soil for plant resilience [J ] . Agronomy , 2025 , 15 ( 3 ): 513 .

LIU S , SUN R , CAI M , et al . Petroleum spill bioremediation by an indigenous constructed bacterial consortium in marine environments [J ] . Ecotoxicology and Environmental Safety , 2022 , 241 ( 113769 ).

WANG Z , WANG S , HE Q , et al . Ecological design of high-performance synthetic microbial communities: from theoretical foundations to functional optimization [J ] . ISME Communications , 2025 , 5 ( 1 ):

MINTY J J , SINGER M E , SCHOLZ S A , et al . Design and characterization of synthetic fungal-bacterial consortia for direct production of isobutanol from cellulosic biomass [J ] . Proceedings of the National Academy of Sciences , 2013 , 110 ( 36 ): 14592 - 7 .

TROIANO D T , STUDER M H P . Microbial consortia for the conversion of biomass into fuels and chemicals [J ] . Nature Communications , 2025 , 16 ( 1 ): 6712 .

XIA X , WEI Q , WU H , et al . Bacillus species are core microbiota of resistant maize cultivars that induce host metabolic defense against corn stalk rot [J ] . Microbiome , 2024 , 12 ( 1 ): 156 .

LIU C , JIANG M , YUAN M M , et al . Root microbiota confers rice resistance to aluminium toxicity and phosphorus deficiency in acidic soils [J ] . Nature Food , 2023 , 4 ( 10 ): 912 - 24 .

DE LA VEGA-CAMARILLO E , SOTELO-AGUILAR J , RIOS-GALICIA B , et al . Promotion of the growth and yield of Zea mays by synthetic microbial communities from Jala maize [J ] . Frontiers in Microbiology , 2023 , 14 ( 1167839 ).

FLORES-NÚÑEZ V M , CAMARENA-POZOS D A , CHÁVEZ-GONZÁLEZ J D , et al . Synthetic communities increase microbial diversity and productivity of Agave tequilana plants in the field [J ] . Phytobiomes Journal , 2023 , 7 ( 4 ): 435 - 48 .

ATARASHI K , TANOUE T , OSHIMA K , et al . Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota [J ] . Nature , 2013 , 500 ( 7461 ): 232 - 6 .

BUFFIE C G , BUCCI V , STEIN R R , et al . Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile [J ] . Nature , 2015 , 517 ( 7533 ): 205 - 8 .

TANOUE T , MORITA S , PLICHTA D R , et al . A defined commensal consortium elicits CD8 T cells and anti-cancer immunity [J ] . Nature , 2019 , 565 ( 7741 ): 600 - 5 .

ZHOU H , SUN R , NIE X , et al . A clinic-responder-derived defined microbial consortium enhances anti-PD-1 immunotherapy efficacy in mice [J ] . Nature Microbiology , 2026 , 1 - 15 .

PACHECO A R , PAUVERT C , KISHORE D , et al . Toward FAIR representations of microbial interactions [J ] . MSystems , 2022 , 7 ( 5 ): e00659-22 .

O'BRIEN E J , MONK J M , PALSSON B O . Using genome-scale models to predict biological capabilities [J ] . Cell , 2015 , 161 ( 5 ): 971 - 87 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research progress and development of the biosynthesis of 1，4-butanediol by Escherichia coli

Synthetic biology drives photosynthetic production of unnatural compounds

AI + synthetic biology： a paradigm shift in biomanufacturing

Opportunities， challenges， and regulatory drivers for the commercial biomanufacturing of novel food ingredients： a case study of L-ergothioneine

Invest early， less， and smartly—a study of synthetic biology R&D investment strategies

Related Author

No data

Related Institution

No data

⁰