Algorithms for Integrative Systems Microbiology: Multi-Dataset and Multi-Omics Integration for Mechanistic Insight

Systems microbiology now generates data at unprecedented scale across microbial community composition and function, host biology, and environmental exposures. Yet the field still faces a core methodological bottleneck: how to integrate heterogeneous datasets, across omics layers, cohorts, populations, and study designs, while preserving biological meaning, controlling confounding, and enabling reproducible inference. Integration remains difficult because microbiome and host datasets often differ in batch effects, sparsity, compositional structure, missingness, sampling depth, and metadata quality. As a result, progress depends on algorithmic advances that support robust data fusion, transferable models, and interpretable outputs that enable testable, systems-level insight across contexts.This Research Topic aims to collect methodological and computational contributions that develop, extend, or rigorously evaluate algorithms for integrating multiple datasets relevant to microbiome and host systems biology. We welcome work that improves cross-study learning, multi-omics fusion, interpretability, and validation, helping to translate integrated microbiome science into mechanistic understanding and practical applications.We welcome submissions centered on algorithms, mathematical/statistical frameworks, and computational methods for integrative systems microbiology, including (but not limited to):• Algorithms for multi-omics integration in microbiome research (metagenomics, metatranscriptomics, metabolomics, proteomics, host omics)• Cross-cohort / cross-population integration and model transferability (e.g., domain adaptation, hierarchical models, meta-analytic learning)• Methods addressing microbiome-specific challenges: filtering strategies, compositionality, sparsity/zero inflation, batch effects, discrimination vs correlation tradeoff, missingness, and confounding• Causal, mechanistic, or interpretable integration frameworks (e.g., causal graphs, dynamic models, pathway- or constraint-informed integration)• Network-based and systems approaches for integrated microbiome–host–environment modeling• Benchmarking and best practices: standardized evaluations, robustness analyses, uncertainty quantification, reproducibility, and reporting standards• Integration of microbiome data with exposome/metadata (diet, geography, migration, socioeconomic variables, clinical context), where the primary contribution is the integrative methodWe welcome Methods, Original Research, Reviews, Perspectives article types. Topic editors: Umer Zeeshan Ijaz, Michelina Ruocco, Jiaxu Li, Xiaogang Wu Source: https://www.frontiersin.org/research-topics/78157/algorithms-for-integrative-systems-microbiology-multi-dataset-and-multi-omics-integration-for-mechanistic-insight