Computational and multi-omics systems biology for precision microbiome therapeutics

The human gut microbiome represents a complex and dynamic therapeutic target whose effective interrogation requires system-level analytical approaches beyond single-omics or reductive methods. This mini-review synthesizes recent advances in computational modeling and multi-omics integration relevant to the development of predictive, patient-tailored microbiome therapies. We critically assess the analytical strengths and limitations of genome-scale metabolic models (GEMs); generalized Lotka-Volterra and ODE-based community models; agent-based simulations; and statistical machine-learning frameworks and examine how their integration with metagenomics, metatranscriptomics, metaproteomics, and metabolomics can help bridge microbial functional potential with clinically relevant phenotypes. Representative applications-including MintTea for disease module identification, gNOMO2 for integrative microbiome profiling, and AGORA-based community metabolic modeling-illustrate the translational scope of these frameworks across inflammatory, metabolic, and infectious disease contexts. Hybrid ML-GEM frameworks have not yet been directly applied to FMT outcome prediction; however, the mechanistic principles underlying both approaches - metabolic compatibility modeling and data-driven responder stratification - suggest a compelling direction for future investigation, contingent on prospective validation in adequately powered and independent clinical cohorts. Persistent methodological challenges-such as data heterogeneity, batch effects across sequencing platforms, incomplete multi-omics coverage, and limited interpretability of complex machine-learning models-are being actively addressed through standardized preprocessing pipelines, explainable Artificial intelligence (AI) strategies, and federated analytics. While federated approaches enable privacy-preserving, multi-institutional model training, they introduce additional constraints related to non-identically distributed data, communication overhead, and uneven computational capacity. Overall, the convergence of mechanistic modeling, data-driven learning, and distributed analytical infrastructures may assist in advancing microbiome research from a largely correlational perspective toward mechanistic and ultimately prescriptive frameworks for precision microbiome medicine.