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Research Abstracts Online
January 2008 - March 2009

University of Minnesota Twin Cities
Institute of Technology
Department of Computer Science and Engineering

PI: Chad L. Myers

Analysis and Inference of Large-scale Genetic Interaction Networks

Understanding the complexity of biological systems and how it relates to cellular function is one of the fundamental questions of modern molecular biology. A classical approach to characterizing complexity and system organization is to probe a cell with combinations of genetic perturbations and observe the resulting phenotype. Such combinatorial perturbations can reveal interactions between genes, which expose structural properties of the underlying genetic network and suggest mechanisms by which cells buffer themselves against genetic variation. Thus, global characterization of genetic interactions in model organisms will have relevance to a number of fundamental biological and medical questions. However, even with recent high-throughput technology for probing genetic interactions, the combinatorial space of possible mutants makes comprehensive investigation of the interaction network a daunting task.

One alternative to exhaustive experimental exploration of the combinatorial space of possible genetic perturbations in an organism is to build predictive models of interaction based on existing genomic data. Such approaches will enable more efficient characterization of system organization and also reveal the fundamental principles governing genetic interactions in the cell. These researchers are developing machine learning and data mining approaches to this in problem and applying them in collaboration with a yeast genetics lab to predict and understand genetic interaction networks in Saccharomyces cerevisiae.

Group Members

Roman Briskine, Graduate Student
Yungil Kim, Graduate Student
Benjamin VanderSluis, Graduate Student