Transcriptional gene regulation is a dynamic process and its proper functioning is essential for all living organisms. By combining the abundant static regulatory data with time series expression data using an Input-Output Hidden Markov model (IOHMM) we were able to reconstruct a dynamic representations for these networks in multiple species. The models lead to testable temporal hypotheses identifying both new regulators and their time of activation. We have recently extended these methods to allow the modeling of various aspects of post-transcriptional regulation including temporal regulation by microRNAs and linking signaling and dynamic regulatory networks. The reconstructed networks link receptors and proteins that directly interact with the environment to the observed expression outcome. I will discuss the application and experimental validation of predictions made by our methods focusing on stress response in yeast, lung development in mice. and human flu response. I would also mention a number of other extensions which we have used to study disease progression and the regulation of immune response.
Lane Center for Computational Biology Seminar
Machine Learning Department, Lane Center for Computational Biology, and Biological Sciences
Carnegie Mellon University
Reconstructing dynamic regulatory networks in development and disease
Friday, November 8, 2013 - 11:00am
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