The human microbiome represents a vastly complex ecosystem that is tightly linked to many host-related processes and that directly impact human health. To date, however, most studies have focused on characterizing the composition of the microbiome in health and in disease and on comparative analyses, and relatively little effort has been directed at studying and modeling the microbiome as an integrated and comprehensive biological system. In this talk, I will highlight the pressing need for the development of predictive system-level models of the microbiome, and present potential computational frameworks for metagenomic-based modeling. I will describe several approaches to model the microbiome at the cellular, ecological, and supra-organismal levels, and will demonstrate the use of such models for predicting microbe-microbe interactions and for designing targeted microbiome interventions. I will further introduce several novel computational methods for linking genomic, metagenomic, and taxonomic information and for integrating multiple meta-omic datasets, aiming to obtain a comprehensive, multi-scale, mechanistic understanding of the microbiome.