Genetic and Molecular Networks
Our goal is to identify genes causing insulin resistance in humans in order to find new therapeutic targets for diabetes and cardiometabolic diseases. Our approach to discovery is grounded in human genetics, clarified through systematic, high throughput experimentation in human cells, and calibrated by its relevance to clinical disease. We use massively parallel genome engineering to re-create mutations identified in patients and develop high-throughput assays to interrogate function in human cell models. We apply bioinformatics and statistics to make sense of this data integrating 1) human mutations, 2) cellular function, and 3) metabolic/glycemic phenotypes of the individuals who harbor them. Using this approach, we have discovered novel missense mutations that greatly increase risk for type 2 diabetes. As a complementary aim towards precision medicine, we develop tools for clinical genome interpretation powered by high-throughput experimental data.
Our lab applies our expertise in human pluripotent stem cell research and genomics to understand the molecular mechanisms underlying normal and abnormal human development, in order to improve the health of mothers and babies.
We are interested in the analysis and modeling of the three-dimensional chromatin structure from high-throughput sequencing experiments. We develop methods that are based in statistics, machine learning, optimization and graph theory to understand how changes in the 3D genome affect cellular outcome such as development, differentiation and gene expression. We have ongoing interests in the systems level analysis and reconstruction of regulatory networks, inference of enhancer-promoter contacts, predictive models of gene expression and integration of three-dimensional chromatin structure with one-dimensional epigenetic measurements in the context of cancer, malaria, asthma and several autoimmune diseases.
Our overall goal is to understand how chromatin structure is employed in making cellular fate decisions, its dynamics, and how it is shaped and maintained by different chromatin regulators (CRs). We merge basic biology, genomics and technology development.
The lab has a variety of bioinformatics projects aimed at improving understanding of the functional impact of autism mutations derived from exome and genome sequencing of the patients. We build spatio-temporal gene co-expression and protein interaction networks for psychiatric diseases and we use these networks to generate the testable hypothesis about the mechanisms of disease. We also test these hypothesis experimentally in the lab, thereby adding a translational aspect to our work.