Statistics Seminars: Stochastic Modelling and Simulation Regimes for Gene Regulation
19 April 2010 14:15 in CM221
The framework of chemical kinetics is now widely used to model activities taking place in the cell. In cases where some species are present at low copy numbers, discrete stochastic modelling is appropriate. However, for reasons of computational efficiency, multi-scale or `hybrid' models that incorporate real-valued stochastic or even deterministic components are attractive.
I will consider two very simple scenarios where analytical insights are possible. First, focusing on mean exit times, I will study the extent to which a real-valued diffusion (Langevin) process can approximate a discrete birth-and-death (Gillespie) process. Second, I will look at a few basic transcription/translation models in gene regulation, including some with feedback loops. Here the effects of mixing and matching modelling regimes can also be quantified.
Host: Umberto Picchini
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