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<p>The construction of artificial microbial communities entails the rational design and assembly of multiple microorganisms into tailored consortia, enabling the execution of specific biological functions under controlled conditions. Propelled by advances in synthetic biology, systems biology, and multi-omics technologies, this strategy has emerged as a promising solution to overcome the inherent limitations of monocultures and natural microbial communities—such as narrow metabolic versatility, poor environmental adaptability, and low controllability—thus exhibiting considerable potential in environmental remediation and biomanufacturing for the sustainable production of high-value compounds. To optimize consortium performance, cross-disciplinary approaches have been developed, including integrating the Design-Build-Test-Learn (DBTL) framework with advanced analytical tools to decipher microbial spatiotemporal interactions, and using AI-driven dynamic modeling to integrate multi-omics data. Future efforts combining dynamic modeling, AI optimization, and cross-disciplinary collaboration will be key to addressing existing challenges. Supplementary measures, such as developing automated equipment and establishing standardized safety frameworks, will further enhance scalability and commercial potential. Ultimately, these advances will fully unlock the capacity of artificial taxa to drive green and sustainable innovations in environmental and industrial fields.</p>

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Advances, opportunities, and challenges in microbial community construction: from environmental remediation to biomanufacturing

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Mengxue Li,
  • Xiang Xing,
  • Kangqing Fei,
  • Zhenni Cheng,
  • Jiayu Zhang,
  • Rongbo Guo,
  • Xiaokui Zhang,
  • Shanfei Fu,
  • Xiaolei Fan

摘要

The construction of artificial microbial communities entails the rational design and assembly of multiple microorganisms into tailored consortia, enabling the execution of specific biological functions under controlled conditions. Propelled by advances in synthetic biology, systems biology, and multi-omics technologies, this strategy has emerged as a promising solution to overcome the inherent limitations of monocultures and natural microbial communities—such as narrow metabolic versatility, poor environmental adaptability, and low controllability—thus exhibiting considerable potential in environmental remediation and biomanufacturing for the sustainable production of high-value compounds. To optimize consortium performance, cross-disciplinary approaches have been developed, including integrating the Design-Build-Test-Learn (DBTL) framework with advanced analytical tools to decipher microbial spatiotemporal interactions, and using AI-driven dynamic modeling to integrate multi-omics data. Future efforts combining dynamic modeling, AI optimization, and cross-disciplinary collaboration will be key to addressing existing challenges. Supplementary measures, such as developing automated equipment and establishing standardized safety frameworks, will further enhance scalability and commercial potential. Ultimately, these advances will fully unlock the capacity of artificial taxa to drive green and sustainable innovations in environmental and industrial fields.