Chad Myers, University of Minnesota, Systems level insights from large-scale analysis of genetic interactions in yeast
Recent developments in experimental technology have enabled the rapid construction and phenotyping of combinatorial genetic perturbations in a number of different organisms. Certain combinations of harmless single perturbations can result in dramatic phenotypes, suggesting redundancy is a ubiquitous property of biological systems. While the data derived from these large-scale screens have proven to be highly informative for characterizing gene function, many challenges remain in the mechanistic interpretation of genetic interactions.
I will describe our recent efforts to understand large-scale genetic interaction networks in the context of the model organism yeast, where we have now measured quantitative phenotypes for more than 10 million double mutants. I will address the general question of how we can learn systems-level biology from these data and demonstrate their utility for characterizing global cellular function and organization. Lastly, I will describe our progress in leveraging insights from the yeast network to improve our understanding and treatment of human disease.
Chad Myers received his Ph.D. from the Department of Computer Science and the Lewis-Sigler Institute for Integrative Genomics at Princeton University in 2007, working with Prof. Olga Troyanskaya. In January 2008, he began as an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota. Dr. Myers’s research emphasis includes computational methods for analysis and interpretation of large-scale genetic interaction networks and methods for integration of diverse genomic data to predict gene function or infer biological networks. His lab is developing approaches for analyzing and leveraging interaction networks to answer biological questions in a variety of systems including yeast, plants (Arabidopsis and maize), worm and human.
Location: Carl Icahn Lab 101
Date/Time: 10/14/13 at 4:15 pm - 10/14/13 at 5:15 pm
Category: Quantitative & Computational Biology
Department: Lewis-Sigler Institute