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Thursday, October 13, 2011
2:00 PM - 3:00 PM
CNLS Conference Room (TA-3, Bldg 1690)

Postdoc Seminar

Systematic Identification of Signal-Activated, Stochastic Gene Regulation

Brian Munsky

Despite vast amounts of biochemical information, it remains difficult to understand or predict the quantitative responses of signal transduction and gene regulation pathways. In this presentation, I discuss new approaches to integrate dynamic single-cell and single-molecule experiments with discrete stochastic analyses. I use these methods to identify models capable of making quantitative predictions for transcriptional dynamics on the level of single cells. I illustrate the power of this approach in a combined experimental/computational investigation of the osmotic stress response pathway in Saccharomyces cerevisiae. After generating several thousand different model structures, we use simple parameter estimation and cross-validation analyses to exclude models that are either too simple or too complex to be supported with the available data. Through a process of iterative experiment design, we eventually select a single quantitative model with the greatest predictive capability. This model yields insight into several dynamical features, including multi-step regulation and low-pass filtering. Furthermore, the model predicts the transcriptional dynamics of cells in response to new environmental and genetic perturbations. Since our approach is general, it can facilitate a predictive understanding for signal-activated transcription in any gene, pathway or organism.

Host: Peter Loxley,