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Center for Nonlinear Studies |
An important goal of systems biology is to understand and predict how cells sense and respond to their environments in the presence of biochemical noise. Although recent studies have revealed many components in these signal transduction and gene regulation pathways, it remains difficult to predict the phenotypic diversity of cellular dynamics. This poster will introduce a comprehensive approach to identify and validate gene regulation models using dynamic single-cell/single-molecule experiments and stochastic analyses. This integrated approach automatically generates hypotheses, proposes optimal experiments, and discards, validates or refines existing hypotheses. Beginning with several thousand hypotheses, we identify a single predictive model of STL1 gene regulation during osmotic shock in yeast. We validate the final model with quantitative predictions at diverse environmental and genetic conditions, which extend well beyond its training data. Our integrated experimental and computational approach is extremely general and applicable to any signal transduction and gene regulation pathway in bacteria, yeast, or human cells.
Host: Peter Loxley, loxley@lanl.gov