ADM-CLE Approach for Detecting Slow Variables in Continuous Time Markov Chains and Dynamic Data
CALIFORNIA UNIV LOS ANGELES DEPT OF MATHEMATICS
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A method for detecting intrinsic slow variables in high-dimensional stochastic chemical reaction networks is developed and analyzed. It combines anisotropic diffusion maps ADMwith approximations based on the chemical Langevin equation CLE. The resulting approach, called ADM-CLE, has the potential of being more efficient than the ADM method for a large class of chemical reaction systems, because it replaces the computationally most expensive step of ADM running local short bursts of simulations by using an approximation based on the CLE. The ADM-CLE approach can be used to estimate the stationary distribution of the detected slow variable, without any a-priori knowledge of it. If the conditional distribution of the fast variables can be obtained analytically, then the resulting ADM-CLE approach does not make any use of Monte Carlo simulations to estimate the distributions of both slow and fast variables.
- Physical Chemistry
- Statistics and Probability
- Operations Research