DNA sequencing has spawned the ?microbiome revolution? -- thousands of microbes and a dizzying number of microbial interactions that are associated with human health and disease. Unfortunately, most species in the microbiome are known only by a (partial) genome. The limited phenotypic data on newly discovered bacteria reveal species that behave unlike any of our model organisms. While genome-scale modeling plays an important role in understanding the microbiome, the paucity of phenotypic data for most species prevents detailed simulation of the microbial communities that affect our health. This project will develop an automated system for profiling, synthesizing, and modeling microbial communities. The center of our approach is Deep Phenotyping, an automated robotic platform that performs complex growth experiments on demand. Data from Deep Phenotyping will be used to train metabolic and statistical models of the oral pathogens Streptococcus mutans and Candida albicans to predict conditions that keep both microbes in a nonpathogenic state.
The microbiome revolution has uncovered thousands of species of bacteria with roles in health and disease. This project automates the identification of interactions between environments, genes, and the microbes that live in and around us. Understanding these interactions is a critical step in re-engineering the microbiome to improve human health.