Results/Conclusions: In this talk, I will highlight the pressing need for the development of systems-level and model-based methods for integrating microbiome-derived multi-omic data. I will further introduce several novel computational frameworks for linking taxonomic, genomic, metagenomic, and metabolomic information about the microbiome. I will specifically discuss FishTaco, an analytical and computational framework that integrates taxonomic and functional comparative analyses to accurately quantify taxon-level contributions to disease-associated functional shifts. Applying FishTaco to several large-scale metagenomic cohorts, I will demonstrate that taxonomic drivers of functional imbalances in the microbiome are function-specific and disease-specific. I will also present MIMOSA, a metabolic model-based approach for integrating microbiome taxonomic and metabolomic profiles and for elucidating mechanistic links between ecological and metabolic variation. Finally, I will discuss the surprising discrepancy between taxonomic and functional variations and will show that revised metagenomic processing can uncover hidden and biologically meaningful functional variation in the human microbiome. Combined, such frameworks lead to an improved comprehensive, multi-scale, and mechanistic understanding of the microbiome in health and disease and of the structure-function relationship in this complex ecosystem. These methods can further inform efforts for personalized microbiome-based therapy and for pinpointing putative intervention targets for manipulating the microbiome’s functional capacity.