Volume 127, Number 5, September 2019
|Number of page(s)||7|
|Published online||10 October 2019|
Stochastic resonance in genetic regulatory networks under Lévy noise
1 School of Mathematics and Statistics, Xi'an Jiaotong University - Xi'an 710049, China
2 School of Mathematics and Statistics, Xuchang University - Xuchang 461000, China
3 School of Mathematics, Southeast University - Nanjing 210096, China
4 Potsdam Institute for Climate Impact Research - Potsdam 14412, Germany
5 Department of Physics, Humboldt University Berlin - Berlin 12489, Germany
Received: 2 July 2019
Accepted: 27 August 2019
It is well known that noise plays an important role in genetic regulatory networks. It can not only affect the overall characteristics of the genetic regulatory networks, but also produce unique functions through the organism organization. In this paper, we use the effect of stochastic resonance (SR) to study the influence of Lévy noise on genetic regulatory networks. The characteristic correlation time tCCT is used to explore the SR phenomenon. We simulate the concentrations changes of mRNAs and repressor-proteins under different parameters, indicating that noise can induce oscillations of mRNAs and repressor-proteins concentrations. We also present SR effects in dependence on the four different intrinsic parameters and Lévy noise intensities. Our results uncover that the trend of noise on the SR of mRNAs and repressor-proteins is similar. We can achieve simultaneously regulation and optimization of mRNAs and repressor-proteins in genetic regulatory through the SR mechanism. We discover that the stability parameter α has a parallel effect on the scale factor γ, and their increase within a certain range can promote the optimal coordination of SR. In addition, the location parameter δ and the skewness parameter β have great benefits for the resonance effect; their increase can suppress the optimal cooperation of SR. Hence, the stimulation of Lévy noise is crucial to gene expression in genetic regulatory networks. The obtained results can provide a viable path for further biological mechanisms.
PACS: 05.45.-a – Nonlinear dynamics and chaos / 05.40.Fb – Random walks and Levy flights / 87.10.Mn – Stochastic modeling
© EPLA, 2019
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