All cells contain a large network of interacting genes and proteins that carry out necessary functions. But we still don’t understand how this network is disrupted in complex diseases like cancer, heart disease, diabetes, or asthma. The Padi lab develops new computational approaches to integrate genomic “big data” and model how cellular networks are functionally altered by disease, genetic variation, epigenetics, and other factors. Our ultimate goal is to identify drivers of disease, propose new therapeutic targets, and predict prognosis and drug response in a patient-specific manner. For example, we are using network analysis to understand how viruses induce tumorigenesis, how genetic variants combine to produce complex phenotypes, and how cancer cell lines and tumors respond to drugs. We use cell culture experiments to refine and validate our predictive network models.
I’ve always been fascinated by using mathematical models to understand the origins of emergent phenomena. I completed my Ph.D. at Harvard University in the field of string theory, the leading contender for a theory of quantum gravity. By studying the way in which black holes emerge from this theory, I was able to shed light on the experimental testability and solution space of string theory, and to take the first steps towards modeling the horizon of astronomical black holes.
Since then, I have focused on understanding how complex phenotypes and diseases emerge from a combination of genomic factors. During my postdoc at Harvard and Dana-Farber Cancer Institute, I developed computational methods for analyzing the network of gene interactions in the cell, in order to predict which genes drive disease, and which features of the network structure represent important pathways and potential drug targets. We also used network analysis to find that tumor viruses can transform cells in ways that mirror the pattern of mutations in non-viral cancers. Following up on this discovery, I developed a panel of cell lines to inducibly express viral oncogenes, and assayed their dynamics during the initial stages of cellular transformation using both RNA-sequencing and single-cell fluorescence time-lapse microscopy. By combining network science and targeted experiments, we aim to understand how genomic perturbations can push cells into a disease state, and to predict and validate therapies that could reverse this process.
I started my group at the University of Arizona in January 2018 and am currently recruiting postdocs and graduate students.