| Abstract: |
| Stochastic reaction networks that exhibit metastable behavior are common
in chemical reaction kinetics, systems biology as well as materials
science. Sampling of the stationary distribution is crucial for
understanding and characterizing the long term dynamics of stochastic
dynamical systems. However, this task is normally
hindered by the insufficient sampling of the rare transitions between
metastable regions. We present parallel replica dynamics for accelerating
simulations of continuous time Markov chains in the presence of
metastability.
We demonstrate that the proposed method accelerates stationary
distribution sampling and yields correct stationary averaging. Furthermore,
we show that it can be combined with path-space information bounds on
path-dependent
functionals and risk sensitive functionals. Such bounds provide error
estimates on quantities of interest as well as bounds on parametric
sensitivity in the complex reaction networks. |
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