Bursting noise in gene expression dynamics: Linking microscopic and mesoscopic models

Abstract

The dynamics of short-lived mRNA results in bursts of protein production in gene regulatory networks. We investigate the propagation of bursting noise between different levels of mathematical modelling, and demonstrate that conventional approaches based on diffusion approximations can fail to capture bursting noise. An alternative coarse-grained model, the so-called piecewise deterministic Markov process (PDMP), is seen to outperform the diffusion approximation in biologically relevant parameter regimes. We provide a systematic embedding of the PDMP model into the landscape of existing approaches, and we present analytical methods to calculate its stationary distribution and switching frequencies.

Manuscript: 11 pages, 9 figures, 1 table. Supplementary Information: 24 pages, 11 figures

References in corpus (3)

• Analytical distributions for stochastic gene expression
• Exact distributions for stochastic gene expression models with bursting and feedback
• Gene expression dynamics with stochastic bursts: exact results for a coarse-grained model

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• Analytic solutions for stochastic hybrid models of gene regulatory networks
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• Effective bandwidth of non-Markovian packet traffic
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• Gene expression noise is affected differentially by feedback in burst frequency and burst size