Foundation Models for Microbial Communities

A Large Multimodal Microbiome Language Model

Abstract

The integration of metagenomics and metabolomics is fundamental to understanding microbial ecosystems, yet is hampered by data heterogeneity and noise. We propose a multimodal foundation model that learns a shared representational language from both data types. This work introduces two core innovations. First, we reframe tokenization as a reinforcement learning problem. A policy learns to construct a unified, hierarchical vocabulary directly from raw sequencing data, guided by a reward function that explicitly balances data quality, statistical coherence, and representational complexity. This process yields semantically rich tokens, ranging from high-quality k-mers to gene-level functional units. Second, a multi-scale architecture fuses local and global context. A Transformer models sample-specific molecular interactions, while a Graph Neural Network (GNN) embeds robust population-level ecological relationships. A dedicated cross-attention fusion module integrates these components, producing a holistic, predictive representation of microbiome function primed for downstream discovery.

Key Contribution

To decipher the microbiome is to learn the very language of living systems—the intricate dialogue that translates genetic potential into functional reality. This requires moving beyond unimodal analysis and embracing the inherent multimodality of biology. However, a brute-force approach to multi-omics is untenable; the sheer scale and noise of the data mandate a paradigm shift from passive data processing to active, intelligent inquiry. We introduce the AI-Controlled Metagenomics framework, or "Thinking Microscope," an autonomous system that co-optimizes data acquisition and computational analysis in a closed loop. The cognitive core of this framework is a novel Multimodal Foundation Model, which learns a unified representational language by integrating metagenomics (the "source code") and metabolomics (the "functional execution"). This model's architecture rests on two pillars. First, a Quality-Aware Tokenizer learns a hierarchical, error-aware vocabulary through reinforcement learning, unlocking noisy public data archives for pre-training at an unprecedented scale. Second, a hybrid Transformer-GNN architecture fuses sample-specific molecular dynamics with population-level ecological principles. By providing the Thinking Microscope with a rich, predictive belief state, this foundation model enables generative data sparsification and principled optimal stopping, achieving massive gains in both efficiency and accuracy. This paper details the theoretical underpinnings of the foundation model that drives this new paradigm of AI-controlled discovery.