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Machine Learning in Microbiome Research and Engineering.

Ryan De Sotto1,2,3,4, Nikhil Aggarwal1,2,3,4, Elizabeth Huiwen Tham1,2,5,6

  • 1NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore.

ACS Synthetic Biology
|December 22, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning (ML) advances microbiome engineering by analyzing complex microbial communities and predicting interactions. This enables targeted manipulation for healthcare and agriculture, overcoming data limitations for future applications.

Keywords:
machine learningmicrobiome engineeringmicrobiome researchsynthetic biology

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Area of Science:

  • Microbiome research
  • Computational biology
  • Synthetic biology

Background:

  • Microbiomes are crucial for healthcare, agriculture, and bioproduction.
  • Engineering microbiomes requires understanding complex microbial interactions.
  • Machine learning (ML) offers powerful tools for analyzing microbiome data.

Purpose of the Study:

  • To examine the transformative applications of ML in microbiome engineering.
  • To highlight ML's role in microbiome data analysis and targeted manipulation.
  • To discuss challenges and future perspectives in ML-driven microbiome engineering.

Main Methods:

  • Utilizing ML algorithms for analyzing large microbiome datasets.
  • Predicting microbial interactions and community behavior.
  • Employing strategies like quorum sensing molecule manipulation, antimicrobial peptides, and bacteriophages for targeted manipulation.

    • Main Results:

    • ML has shown success in identifying microbial communities and predicting interactions.
    • ML aids in optimizing microbial community design for specific functions.
    • Integration of ML with experimental approaches is advancing microbiome engineering.

    Conclusions:

    • ML is revolutionizing microbiome engineering, enabling novel applications.
    • Challenges include data limitations, biological complexity, and integrating computational/experimental methods.
    • Future advancements in data generation, algorithms, and collaboration will unlock microbiome potential.