Linear mapping approximation of gene regulatory networks with stochastic dynamics

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• Ramon Grima, Edinburgh
• Thursday 29 November 2018, 13:00-14:00
• Watson LTB.

If you have a question about this talk, please contact Fabian Spill.

The regulation of gene expression is commonly achieved through transcriptional factor binding to DNA . However the presence of binding reactions often leads to analytically intractable stochastic models of gene expression. I will here introduce the linear-mapping approximation (LMA) that maps systems with protein-promoter interactions onto approximately equivalent systems with no binding reactions. This leads to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method’s accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dynamics. The method is also used to study the first-passage time distribution of promoter switching, the sensitivity of the size of protein number fluctuations to parameter perturbation and the stochastic bifurcation diagram characterizing the onset of multimodality in protein number distributions. I will also discuss how the LMA can be used to reliably infer parameter values of gene regulatory networks with feedback loops from population snapshot data in a fraction of the time of popular Bayesian approaches.

This talk is part of the Applied Mathematics Seminar Series series.

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• Applied Mathematics Seminar Series
• School of Mathematics events
• Watson LTB

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