Title
Most probable dynamics of a genetic regulatory network under stable Lévy noise.
Abstract
Numerous studies have demonstrated the important role of noise in the dynamical behaviour of a complex system. The most probable trajectories of nonlinear systems under the influence of Gaussian noise have recently been studied already. However, there has been only a few works that examine how most probable trajectories in the two-dimensional system (MeKS network) are influenced under non-Gaussian stable Lévy noise. Therefore, we discuss the most probable trajectories of a two-dimensional model depicting the competence behaviour in B. subtilis under the influence of stable Lévy noise. On the basis of the Fokker-Planck equation, we describe the noise-induced most probable trajectories of the MeKS network from the low ComK protein concentration (vegetative state) to the high ComK protein concentration (competence state) under stable Lévy noise. We demonstrate choices of the non-Gaussianity index α and the noise intensity ϵ which generate the ComK protein escape from the low concentration to the high concentration. We also reveal the optimal combination of both parameters α and ϵ making the tipping time shortest. Moreover, we find that different initial concentrations around the low ComK protein concentration evolve to a metastable state, and provide the optimal α and ϵ such that the distance between the deterministic competence state and the metastable state is smallest.
Year
DOI
Venue
2019
10.1016/j.amc.2018.12.005
Applied Mathematics and Computation
Keywords
DocType
Volume
Nonlocal Fokker–Planck equation,Most probable trajectories,Gene regulation,Non-Gaussian stochastic dynamics,Lévy noise
Journal
348
ISSN
Citations 
PageRank 
0096-3003
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Xiaoli Chen131.05
Fengyan Wu200.34
Jinqiao Duan32315.58
Jürgen Kurths42000142.58
xiaofan li57912.44