ADM-CLE APPROACH FOR DETECTING SLOW VARIABLES IN CONTINUOUS TIME MARKOV CHAINS AND DYNAMIC DATA

Author: 

Cucuringu, M
Erban, R

Publication Date: 

2017

Journal: 

SIAM JOURNAL ON SCIENTIFIC COMPUTING

Last Updated: 

2019-08-27T01:49:58.863+01:00

Issue: 

1

Volume: 

39

DOI: 

10.1137/15M1017120

page: 

B76-B101

abstract: 

A method for detecting intrinsic slow variables in high-dimensional
stochastic chemical reaction networks is developed and analyzed. It combines
anisotropic diffusion maps (ADM) with 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.

Symplectic id: 

517507

Download URL: 

Submitted to ORA: 

Submitted

Publication Type: 

Journal Article