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J Theor Biol. 2013 Oct 21;335:222-34. doi: 10.1016/j.jtbi.2013.06.021. Epub 2013 Jul 2.

Signatures of nonlinearity in single cell noise-induced oscillations.

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  • 1School of Biological Sciences, University of Edinburgh, United Kingdom.

Abstract

A class of theoretical models seeks to explain rhythmic single cell data by postulating that they are generated by intrinsic noise in biochemical systems whose deterministic models exhibit only damped oscillations. The main features of such noise-induced oscillations are quantified by the power spectrum which measures the dependence of the oscillatory signal's power with frequency. In this paper we derive an approximate closed-form expression for the power spectrum of any monostable biochemical system close to a Hopf bifurcation, where noise-induced oscillations are most pronounced. Unlike the commonly used linear noise approximation which is valid in the macroscopic limit of large volumes, our theory is valid over a wide range of volumes and hence affords a more suitable description of single cell noise-induced oscillations. Our theory predicts that the spectra have three universal features: (i) a dominant peak at some frequency, (ii) a smaller peak at twice the frequency of the dominant peak and (iii) a peak at zero frequency. Of these, the linear noise approximation predicts only the first feature while the remaining two stem from the combination of intrinsic noise and nonlinearity in the law of mass action. The theoretical expressions are shown to accurately match the power spectra determined from stochastic simulations of mitotic and circadian oscillators. Furthermore it is shown how recently acquired single cell rhythmic fibroblast data displays all the features predicted by our theory and that the experimental spectrum is well described by our theory but not by the conventional linear noise approximation.

© 2013 Elsevier Ltd. All rights reserved.

KEYWORDS:

Intrinsic noise; Linear noise approximation; Master equations; Oscillations; Stochastic differential equations

PMID:
23831270
[PubMed - indexed for MEDLINE]
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