Learning Dynamic Maps of Temporal Gene Regulation

Jason Ernst

Abstract

Time series microarray gene expression experiments have become a widely used experimental technique to study the dynamic biological responses of organisms to a variety of stimuli. The data from these experiments are often clustered to reveal significant temporal expression patterns. These observed temporal expression patterns are largely a result of a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an Input-Output Hidden Markov Model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified dynamic map. Applying our method to study yeast response to stress we derive dynamic maps that are able to recover many of the known aspects of these responses. Additionally the method has made new predictions that have been experimentally validated.

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