From Meta-omics to Mechanisms: Interpretable AI for Microbial Ecology

Microbial ecology is rapidly evolving with the integration of artificial intelligence (AI) and machine learning into the study of complex microbial communities. Traditionally, research in this field has focused on cataloging microbial diversity and assessing community composition through various omics technologies. However, significant challenges remain in moving beyond descriptive analyses toward a detailed understanding of interactions, functions, and dynamic processes that govern microbial systems. Recent advancements in AI have enabled the handling and interpretation of massive multi-omics datasets, offering unprecedented opportunities to infer microbial interactions, predict ecosystem functions, and unearth driving mechanisms behind community dynamics. Despite these advances, there remains a critical need for interpretable AI models that go beyond prediction, providing mechanistic insights and hypothesis-driven knowledge applicable across environmental, engineered, host-microbiome and biogeochemical systems, and synthetic communities. This Research Topic aims to showcase innovative methodologies and case studies that leverage interpretable AI and machine learning to elucidate the mechanistic underpinnings of microbial communities. Interpretable AI is defined by the ability to understand and explain the decisions and predictions done by artificial intelligence models. The central objective is to foster work that not only utilizes amplicon sequencing and multi-omics integration but also moves toward causality, hypothesis testing, and predictive modeling. We seek contributions that provide clarity on microbial interaction networks, spatiotemporal patterns, and ecosystem-level processes, with a particular emphasis on methods that enhance interpretability, transparency, and biological relevance.The scope is confined to research deploying AI and machine learning for mechanistic or predictive insights into microbial ecology, specifically when these methods contribute to understanding interactions, functions, or dynamics within microbial systems. Studies should address clear hypotheses and promote mechanistic interpretation rather than solely descriptive outcomes. To gather further insights in this area, we welcome articles addressing, but not limited to, the following themes:• Application of interpretable AI models to infer microbial interactions and networks• Multi-omics integration for functional and ecosystem-level predictions• Causal inference and hypothesis-driven approaches in microbial ecology• AI-driven frameworks for spatial and temporal analysis of microbial communities• Translating predictive models into actionable understanding in environmental, engineered, or biogeochemical contexts• Graph neural networks and topological approaches for microbial interaction inference• Benchmarking and reproducibility frameworks for AI-driven microbiome analyses• Integration of ecological theory with data-driven models (e.g., ecological network stability, niche theory)Please note that Systems Microbiology does not consider descriptive studies that are solely based on amplicon (e.g., 16S rRNA, 18SrRNA, ITS and other marker genes) profiles, unless they are accompanied by a clear hypothesis and experimentation and provide insight into the microbiological system or process being studied. Topic editors: Tong Liu, Wei Qiao, Sasa Zuo, Bao Qian Source: https://www.frontiersin.org/research-topics/78829/from-meta-omics-to-mechanisms-interpretable-ai-for-microbial-ecology