Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models

Abstract

Microbiome sequencing has advanced faster than microbiome understanding. Although large‐scale 16S, metagenomic, metatranscriptomic, and proteomic datasets have accumulated rapidly, most analyses remain cohort‐specific and association‐driven, limiting mechanistic insight, cross‐study transferability, and robustness to technical confounding. Foundation models offer a new computational framework by learning reusable biological representations from large unlabeled datasets. In this Review, we present microbiome foundation models as a hierarchy spanning biological scales. Sequence‐centric models capture the syntax and semantics of DNA and proteins for taxonomic inference, functional annotation, and generative design. Community‐centric models learn ecological structure from abundance profiles, while addressing compositionality, sparsity, and the unordered nature of microbial communities. Emerging multimodal frameworks integrate sequence‐derived functional potential with community‐level ecological dynamics under host and environmental context. We discuss key design choices, including tokenization, representation granularity, self‐supervised objectives, and evaluation strategies, and highlight challenges in interpretability, domain shift, causal reasoning, and biological validation. Finally, we propose a transition from static representation learning toward intervention‐aware microbiome world models capable of simulation, digital twinning, and generative microbiome engineering.