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June 2018 Large deviations theory for Markov jump models of chemical reaction networks
Andrea Agazzi, Amir Dembo, Jean-Pierre Eckmann
Ann. Appl. Probab. 28(3): 1821-1855 (June 2018). DOI: 10.1214/17-AAP1344

Abstract

We prove a sample path Large Deviation Principle (LDP) for a class of jump processes whose rates are not uniformly Lipschitz continuous in phase space. Building on it, we further establish the corresponding Wentzell–Freidlin (W-F) (infinite time horizon) asymptotic theory. These results apply to jump Markov processes that model the dynamics of chemical reaction networks under mass action kinetics, on a microscopic scale. We provide natural sufficient topological conditions for the applicability of our LDP and W-F results. This then justifies the computation of nonequilibrium potential and exponential transition time estimates between different attractors in the large volume limit, for systems that are beyond the reach of standard chemical reaction network theory.

Citation

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Andrea Agazzi. Amir Dembo. Jean-Pierre Eckmann. "Large deviations theory for Markov jump models of chemical reaction networks." Ann. Appl. Probab. 28 (3) 1821 - 1855, June 2018. https://doi.org/10.1214/17-AAP1344

Information

Received: 1 January 2017; Revised: 1 August 2017; Published: June 2018
First available in Project Euclid: 1 June 2018

zbMATH: 06919739
MathSciNet: MR3809478
Digital Object Identifier: 10.1214/17-AAP1344

Subjects:
Primary: 60F10 , 80A30
Secondary: 37B25 , 60J75

Keywords: chemical reaction networks , jump Markov processes , large deviation principle , Lyapunov functions , toric jets , Wentzell–Freidlin theory

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.28 • No. 3 • June 2018
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