Diagnostics for assessing the linear noise and moment closure approximations

Gillespie Colin S. () and Golightly Andrew
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Gillespie Colin S.: Newcastle University – School of Mathematics and Statistics, Newcastle, United Kingdom of Great Britain and Northern Ireland
Golightly Andrew: Newcastle University – School of Mathematics and Statistics, Newcastle, United Kingdom of Great Britain and Northern Ireland

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 5, 363-379

Abstract: Solving the chemical master equation exactly is typically not possible, so instead we must rely on simulation based methods. Unfortunately, drawing exact realisations, results in simulating every reaction that occurs. This will preclude the use of exact simulators for models of any realistic size and so approximate algorithms become important. In this paper we describe a general framework for assessing the accuracy of the linear noise and two moment approximations. By constructing an efficient space filling design over the parameter region of interest, we present a number of useful diagnostic tools that aids modellers in assessing whether the approximation is suitable. In particular, we leverage the normality assumption of the linear noise and moment closure approximations.

Keywords: approximate simulator; linear noise; moment-closure; stochastic kinetic model (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/sagmb-2014-0071

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