American Society for Cell Biology
Poster: Monday, December 3, 2007, program 1216, board B356
Presented 1:30p-3:00p by Tim Andersen.
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Development and Validation of
Functional Gene Regulatory Network Models
Poster image (reduced size suitable for page printing)
An important goal of computational genomics is the development of in silico modeling techniques that make accurate predictions about the behavior of gene regulatory networks (GRNs) in vivo. To this end we have developed a computational platform that accurately simulates the activity of arbitrary GRNs, and predicts how a GRN will react to gene knockouts, alteration of gene promotion strengths, or other perturbation. The platform supports manually encoding GRNs, as well as automated creation and refinement of GRNs using an evolutionary search process. Using this platform we have developed a model of the transcriptional regulatory pathway downstream of SWI4 that mediates stress response in yeast. The model was fine-tuned using evolutionary search. The search seeks to minimize the difference between the simulated gene expression and in vivo microarray data. The model was then validated by leave-one-out cross validation, comparing the predicted output of 34 genes involved in the regulatory pathway against microarray data obtained from five in vivo gene knockout experiments. The results show that the implemented GRN precisely matches the established functional annotations (promotion vs inhibition) of the yeast transcriptional regulatory pathways downstream of SWI4. In addition, predictions of gene activity were quite accurate, as measured by the average weighted absolute difference (range: 0.03 to 0.21) between the predictions of the simulated GRN and the log ratios obtained from the in vivo knockout experiments for HAP4, MSN4, YAP6, SOK2, and SWI4. This accuracy demonstrates that the platform is capable of guiding in vivo experiment design by supporting accurate, rapid experiment prototyping and prediction for GRNs.
CDR is presenting three other posters as ASCB: