Today, we have the ability to measure the transcriptional activity of genes, identify mutations, and quantify the protein and metabolite content of our cells. All of these measurements can be made at the genome-level. These data present the potential to greatly improve our ability to characterize and treat disease; however, the rate of data production is far outpacing our ability to analyze, interpret, and ultimately build predictive tools in medicine. My lab takes a complementary dry and wet lab approach to close the gap between raw data and biological understanding. Our dry lab research focuses on developing and implementing computational tools that distill this large pool of genome-scale data into actionable hypothesis. Our wet lab research brings the computational modeling to the bench where we aim to characterize the genomic components that contribute to drug mode of action in cancer biology.
Within the broad scope of systems biology, my lab focuses on 3 research areas: 1) Network inference for identifying drug targets, 2) Predicting drug sensitivity from -omics datasets, and 3) Modeling temporal effects of drug combinations. The reason we are able to study these topics is from the generous work of thousands of scientists across the world that help generate, annotate, and manage invaluable datasets. These projects include, but are not limited to, the Cancer Genome Atlas (TCGA), Cancer Cell Line Encylopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC), and the Connectivity map.
Network inference for identifying drug targets: Network inference tools aim to identify the most relevant dependencies that exist between any two elements given a large set of data. In particular, we explore the relationship between transcriptional regulatory genes and the biological pathways they regulate. Genes that control dysregulated pathways in disease become potential targets for therapeutic intervention. We develop tools that effectively identify these target genes.
Predicting drug sensitivity from –omics datasets: A central goal of personalized medicine is to determine treatment for patients given their genomic backgrounds. We are far from systematically accomplishing this goal with patients, thus we use human cell lines as a proxy to study the interactions between cells and drugs. We focus on understanding the genomic factors that contribute to drug response or resistance.
Modeling temporal effects of drug combinations: The study of drugs and drug combinations often involves measuring dead versus live or growing cells, yet there are many other endpoints that can be measured, for example, the activation of disease-related signaling pathway. Additionally, drug combinations can have synergistic or antagonistic results depending on which drug is administered first. We focus on integrating experimental and computational approaches to model the effects of drug treatment as a function of different endpoint measurements and the order and timing of administration.
In additional to these primary research areas, we are active in the organization of Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenges. The DREAM project is organized around a community of data scientists, where high-impact data along with challenges are presented annually, participants submit their best solutions, and assessment is performed using standardized metrics and blinded gold standards. The product of this effort is a rigorous assessment and performance ranking of the current best methods to address a challenge, along with cultivating a community of scientists interested in biomedical research problems.