Our lab marries computation with experiments to study the molecular evolution of genes and pathways. We seek broadly to characterize biological mechanisms and functions, and how they may be corrupted by genetic mistakes or re-engineered to new purpose. The long-term goals are to design new personalized therapies and to harness the synthetic potential of organisms. Shorter term goals are to interpret the action of human genome variations on health and disease.
Our algorithms broadly merge mathematical and evolutionary principles together with machine learning and artificial intelligence. As a result, they enable multi-scale data integration and precise control of molecular functions. This has led to discoveries in diverse systems, from E. Coli to Humans, including in drug resistance, G protein signaling, malaria and cancer. Starting from structural bioinformatics, newer interests now include network theory, text-mining and cognitive computing. Technically, we draw upon a wide range of disciplines to address fundamental questions in structural biology, clinical genomics and precision medicine.
Specific examples includes a network compression scheme that made tractable the diffusion of information across nearly 400 species. This approach uncovered a possible mechanism for the best current drug against malaria. Other network studies, reasoned over the entire PubMed literature to discover new kinases and protein interactions for p53. A distinct line of research quantifies the evolutionary action (EA) of mutations on fitness and bridges between molecular and population genetics. EA correlates with experimental loss of function in proteins; with morbidity and mortality in people; and with purifying gene selection in population. In some head and neck cancer patients, it stratifies outcomes and suggests alternate therapy for some patients. In the coming years we hope to unite these different approaches into a coherent path to precision therapy personalized to patients on a case by case basis.
