Our lab studies the molecular mechanisms underlying complex human diseases. We use genomics techniques to study the intracellular networks involved in the pathogenesis of autoimmune and infectious diseases. Our ultimate goal is to develop predictive models of the intracellular networks involved in pathogenesis. Such models may help us to better understand the basic biology of disease and predict cellular response to drugs.
We have a major interest in studying how variations in the genomes of individuals shape their phenotypes. Associating DNA sequence variation with variation in quantitative phenotypes ignores all of the intermediate chain of reactions from genetic perturbation to phenotype. In particular, intermediate molecular phenotype such as coding and non-coding transcript abundance has high variation, and might fill the gap between genotype and phenotype. We wish to understand whether and how genetic variation is translated into phenotypic diversity through transcript abundance variation in health and disease. Understanding these issues will be enormously important for understanding why different people respond differently to specific drugs, an important step toward personalized medicine. To that end, we develop genetic modeling (‘personalized modeling’) approaches that integrate genotyping with RNA-seq, ChIP-seq and multiplexed measurements of gene signatures to construct a system-level quantitative trait locus mapping framework. Currently, we are particularly interested in the variability in inflammatory transcript abundance and its influence on phenotypic diversity in innate immunity.
The group will consist of biologists, computer scientists and mathematicians. We focus on extensive computational work and development of new algorithms for studying the basis of human disease. We believe that an effective approach to develop a predictive personalized models of disease needs to measure multiple pathogen stimuli, environmental conditions, drug treatments, developmental time points and tissues, and simultaneously map multiple functional mechanisms using probabilistic mechanistic models. We work toward these goals by developing unique computational approaches and through close collaborations with experimental labs in immuno-genomics.