A stochastic framework for discrete models in systems biology

David Murrugarra (September 1, 2011)

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Abstract

This talk will introduce a new modeling framework for gene regulatory networks that incorporates state dependent delays and that is able to capture the cell-to-cell variability. This framework will be presented in the context of finite dynamical systems, where each gene can take on a finite number of states, and where time is also a discrete variable. The state dependent delays represent the time delays of activation and degradation. One of the new features of this framework is that it allows a finer analysis of discrete models and the possibility to simulate cell populations. Applications presented will use one of the best known stochastic regulatory networks, that is involved in controlling the outcome of lambda phage infection of bacteria.