Humans are not individual organisms. Rather, we are complex, dynamic ecosystems, comprised of interacting communities of human cells and a wide variety of microorganisms. Without these symbiotic microbial communities, our existence, and perhaps the existence of all complex forms of life, would be impossible. Recently, the profound impact of these communities on human health has begun to be understood, made possible by the advent of ultra-high throughput sequencing. In many regards, opening these communities to investigation has only highlighted the tremendous diversity in microbial populations from host to host and over time in the same host. This diversity makes associating specific microbial species with their effects on host health difficult. Here, we propose a set of integrated computational biology tools that link bacterial sensor networks, microbiome community interactions, and microbiome metabolome to host health. Using previously published genomic and metagenom ic datasets from bacterial, human, and mouse-model microbiome experiments, we have generated computational models that span multiple biological scales of host-microbiome interactions: the interaction between an individual bacterium’s metabolome and transportome with their host, a microbiome community’s metabolome influence on host health, and a dynamic model of the interactions between microbiome community, host, and host diet. Together, these elements will be used to generate a comprehensive model, iMOUSE, which can be used to reliably predict the results of biological experiments in silico and to identify an optimal diet for a host based on the host’s current microbiome. The computational models we have developed can be expanded far beyond the specific datasets analyzed here to a wide variety of host-microbiome interactions.