NDLI: Computation of switch time distributions in stochastic gene regulatory networks

Content Provider	IEEE Xplore Digital Library
Author	Munsky, B. Khammash, M.
Copyright Year	2008
Description	Author affiliation: Center for Control, Dynamical Syst. & Comput., Univ. of California, Santa Barbara, CA (Munsky, B.; Khammash, M.)
Abstract	Many gene regulatory networks are modeled at the mesoscopic scale, where chemical populations are assumed to change according a discrete state (jump) Markov process. The chemical master equation (CME) for such a process is typically infinite dimensional and is unlikely to be computationally tractable without further reduction. The recently proposed Finite State Projection (FSP) technique allows for a bulk reduction of the CME while explicitly keeping track of its own approximation error. In previous work, this error has been reduced in order to obtain more accurate CME solutions for many biological examples. In this paper, we show that this "error" has far more significance than simply the distance between the approximate and exact solutions of the CME. In particular, we show that apart from its use as a measure for the quality of approximation, this error term serves as an exact measure of the rate of first transition from one system region to another. We demonstrate how this term may be used to directly determine the statistical distributions for stochastic switch rates, escape times, trajectory periods, and trajectory bifurcations. We illustrate the benefits of this approach to analyze the switching behavior of a stochastic model of Gardner's genetic toggle switch.
Starting Page	2761
Ending Page	2766
File Size	400215
Page Count	6
File Format	PDF
ISBN	9781424420780
ISSN	07431619
DOI	10.1109/ACC.2008.4586911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Switches Computer networks Distributed computing Stochastic processes Chemical processes Particle measurements Markov processes Equations Biology computing Approximation error
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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