Sensitivity Estimation and Inverse Problems in Spatial Stochastic Models of Chemical Kinetics
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We consider computational stochastic modeling of diffusion-controlled reactions with applications mainly in molecular cell biology. A complication from the traditional ‘well-stirred’ case is that our models have a spatial dimension. Our aim here is to put forward a practical algorithm by which perturbations can be propagated through these types of simulations. This is important since the quality of experimental data calls for frequently estimating stability constants. Another use is in inverse formulations which generally relies on being able to effectively and accurately judge the effects of small perturbations. For this purpose we present our implementation of an “all events method” and give two concrete examples of its use. One case studied is the effect of stochastic focusing in the spatial setting, the other case treats the optimization of a small biochemical network.
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