A multi-stage stochastic model of regulatory control in pluripotentstem cells

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Models of gene regulatory networks are essential to understand cellular processes from a mechanistic perspective. Present models of self-renewal and differentiation of embryonic stem cells focus on the transcriptional regulation between the key pluripotency factors. However, recent reports pinpoint additional layers of regulatory control, most importantly chromatin remodeling and post-transcriptional regulation via microRNAs. The linkage of these different layers of regulatory control and its implications on pluripotency are far from understood. Here, we propose the development of a model of pluripotency, which explicitly considers the regulation of DNA activity and mRNA stability via a multi-stage stochastic description of gene expression. With the expertise of the host laboratories in stochastic modeling of gene expression (Swain) and in the molecular foundations of pluripotency (Chambers) and the applicant"s background in analyzing regulatory control, we will set up a detailed, biochemically motivated model. We will analyze the effects of noise by simulating the system with Markov jump processes. With expression data from Ian Chambers" laboratory, we want to estimate systemic parameters and identify processes on different time scales. These observations justify approximations in the analytical description of the system. We will delineate molecular abundances and distributions with analytical methods and compare them to the data. Finally, we want to anticipate the effects of specific system perturbations and design experiments to test our predictions. With this interdisciplinary effort we want to get insights into the multi-layered regulatory control of pluripotency.