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Home | Seminars and Symposia | Past seminars/symposia: Wednesday, May 18, 2011

DTC Leading Edge Seminar Series

Learning about the cell by breaking it: large-scale analysis of combinatorial perturbations in yeast

by

Chad Myers
Computer Science and Engineering
University of Minnesota

Wednesday, May 18, 2011
3:30 p.m. reception
4:00 p.m. seminar

401/402 Walter Library

View webcast of this seminar

MyersA classical genetic approach to understanding biological systems is through targeted perturbation. Disabling specific components (genes) of the cell and observing the resulting phenotype can be a powerful means of understanding gene function. An unexpected result of many such single-gene perturbation experiments in model organisms is that they often result in surprisingly subtle phenotypes. A large fraction of the genes simply have little or no observable effect when deleted independently. Thus, recent attention has focused on introducing combinations of perturbations simultaneously. Interestingly, certain combinations of harmless single perturbations can result in dramatic phenotypes, suggesting built-in network redundancy is a ubiquitous property of cellular systems. Recent experimental technology has given us a relatively global view of this phenomenon by enabling the measurement of millions of combinations of perturbations. However, along with their power for characterizing network structure, comes complexity both in the space of possible perturbations as well as in the interpretation of increasingly large interaction datasets. In this talk, I will describe our recent efforts to understand the results of large-scale combinatorial perturbation experiments in the context of the model organism yeast. In collaboration with a yeast genetics lab, we have measured quantitative phenotypes for millions of double deletion mutants. I will address the general question of how we can learn systems-level biology from these experiments and demonstrate their utility for characterizing global cellular function and organization. Finally, I will highlight several open problems in the interpretation of genetic interaction networks and discuss where innovations in machine learning and data mining are particularly relevant.

 

Chad Myers received his Ph.D. from the Department of Computer Science at Princeton University in 2007, working with Dr. Olga Troyanskaya in the Lewis-Sigler Institute for Integrative Genomics. In January 2008, he began his current position 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. Website: http://csbio.cs.umn.edu/