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Mean First Passage Time and Stochastic Resonance in a Transcriptional Regulatory System with Non-Gaussian Noise

Yan-Mei Kang

School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, P. R. China

E-mail Address: ymkang@mail.xjtu.edu.cn

Corresponding author.

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Xi Chen

School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, P. R. China

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Xu-Dong Lin

School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, P. R. China

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, and

Ning Tan

School of Aerospace, Xi’an Jiaotong University, Xi’an 710049, P. R. China

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https://doi.org/10.1142/S0219477517500079Cited by:25 (Source: Crossref)

Abstract

The mean first passage time (MFPT) in a phenomenological gene transcriptional regulatory model with non-Gaussian noise is analytically investigated based on the singular perturbation technique. The effect of the non-Gaussian noise on the phenomenon of stochastic resonance (SR) is then disclosed based on a new combination of adiabatic elimination and linear response approximation. Compared with the results in the Gaussian noise case, it is found that bounded non-Gaussian noise inhibits the transition between different concentrations of protein, while heavy-tailed non-Gaussian noise accelerates the transition. It is also found that the optimal noise intensity for SR in the heavy-tailed noise case is smaller, while the optimal noise intensity in the bounded noise case is larger. These observations can be explained by the heavy-tailed noise easing random transitions.

Communicated by Benjamin Lindner

Keywords:

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Vol. 16, No. 01

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History

Received 13 August 2015

Accepted 8 November 2016

Published: 27 January 2017

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This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.

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Mean First Passage Time and Stochastic Resonance in a Transcriptional Regulatory System with Non-Gaussian Noise

Abstract

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