Eubiota: Modular Agentic AI for Autonomous Discovery in the Gut Microbiome

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Authors: Pan Lu, Y. S. Gao, William G. Peng, Haoxiang Zhang, Kunlun Zhu, Elektra Kantzari Robinson, Qixin Xu, Masakazu Kotaka, Harrison G. Zhang, Bingxuan Li, Anthony L. Shiver, Yejin Choi
+3 more Kerwyn Casey Huang, Justin Laine Sonnenburg, James Zou
Cited by: 2

Abstract

The gut microbiome regulates many aspects of human biology, including immunity and inflammatory diseases, yet mechanistic discovery and translation remain constrained by fragmented workflows and manual hypothesis integration. Here, we present Eubiota, an open-source modular agentic framework that decomposes microbiome inquiry using specialized agents for planning, execution, verification, and grounded synthesis. Coordinated through shared memory and domain-specific tools, Eubiota employs reinforcement learning to optimize multi-turn reasoning, achieving 87.7% benchmark accuracy and outperforming GPT-5.1 by 10.4%. Across four case studies, Eubiota enabled end-to-end discovery with experimental validation. Screening nearly 2,000 bacterial genes, Eubiota identified the uvr-ruv DNA repair axis as a fitness determinant under inflammatory stress, validated using transposon mutants and IBD metagenomes. It further designed a four-strain consortium that attenuated colitis severity in mice and generated a commensal-sparing antibiotic cocktail, demonstrating its utility in addressing community-level design challenges at cellular and molecular levels. Finally, Eubiota discovered diet-associated metabolites that suppress NF-kB signaling. Together, these results establish Eubiota as a scalable, tool-grounded scientific copilot for mechanistically driven microbiome discovery.

Fields of Study & Subjects

Gut microbiota and health Machine Learning in Bioinformatics Bacterial Genetics and Biotechnology

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
28 February 2026
Document type
Preprint