Abstract
Most disease associated genomic variants have relatively modest effects on target gene expression in reporter or CRISPR perturbation assays. In addition, enhancer disruption in vivo often has surprisingly weak phenotypic consequences. I will present machine learning (ML) methods (gkm-SVM and DNN) which we use to learn the complex transcription factor combinations that control enhancer activity and cell fate. I will then use these methods to develop a quantitative model for enhancer activity which shows that while promoter knockdown has robust effects on target gene expression, individual enhancer knockdown is often weaker and affects temporal transition dynamics, but not the final steady state. This model provides an explanation of the paradox of how enhancer variation can be strongly associated with disease risk while having individually weak effects, by showing in detail how gene regulatory networks control developmentally important and disease relevant cell state transitions and cancer.
Background
Dr. Michael Beer is a professor of biomedical engineering and genetic medicine at the Johns Hopkins University School of Medicine. His research focuses on understanding how gene regulatory information is encoded in genomic DNA sequence and how regulatory variation contributes to diseases. His lab has recently developed machine-learning techniques where computer algorithms detect regulatory sequences in intergenic DNA.
Dr. Beer received his undergraduate degree from the University of Michigan. He earned his Ph.D. in astrophysical sciences from Princeton University. Dr. Beer joined the Johns Hopkins faculty in 2005.
Prior to joining Johns Hopkins, Dr. Beer was the Lewis Thomas Postdoctoral Fellow in the Department of Molecular Biology and Lewis-Sigler Institute for Integrative Genomics at Princeton University.
Dr. Beer was recognized with the Simon Ramo Award for his thesis in plasma physics. He also was awarded the DOE Fusion Energy Postdoctoral Fellowship and the National Science Foundation Graduate Fellowship, and the Searle Scholars Award for promising junior facility. He has also been recognized with the Johns Hopkins Alumni Association Excellence in Teaching Award.