My lab focuses on developing and applying novel machine learning and high-throughput experimental techniques to further our understanding of the microbiome, or collection of micro-organisms living on and within us. The microbiome has been implicated in a variety of diseases including infection, allergy, auto-immunity, cancer, diabetes, bowel, cardiovascular, and neurological diseases. Advanced computational methods are essential for making sense of this rich and complex ecosystem residing in our bodies, and a particular focus of my lab is developing machine learning methods to analyze the temporal dynamics of the microbiome for purposes of ultimately improving diagnosis and treatment of patients. Our microbiomes are inherently dynamic, changing due to factors including maturation of the gut in childhood, diet, and environmental exposures; analyzing these dynamics is key to linking the microbiome to disease and predicting the effects of therapies targeting the microbiome. Our work in this area has led to new insights into the temporal response of the microbiome to antibiotics, infectious, and dietary perturbations, as well as furthering the development of bacteria-based therapies for C. difficile colitis and food allergies.
A related interest of my lab is using synthetic and systems biology-based experimental approaches to elucidate and manipulate functional properties of the gut microbiome. Work in this area has included collaborative projects to engineer bacterial strains to detect inflammation in the mammalian gut, manipulating the microbiome using bacteriophages, and a platform for functionally mining bacterial genomes for genes contributing to fitness in the gut.