title: Stochastic synchronization of genetic oscillator networks.
abstract: BACKGROUND
  The study of synchronization among genetic oscillators is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. Genetic networks are intrinsically noisy due to natural random intra- and inter-cellular fluctuations. Therefore, it is important to study the effects of noise perturbation on the synchronous dynamics of genetic oscillators. From the synthetic biology viewpoint, it is also important to implement biological systems that minimizing the negative influence of the perturbations.
  
  
  RESULTS
  In this paper, based on systems biology approach, we provide a general theoretical result on the synchronization of genetic oscillators with stochastic perturbations. By exploiting the specific properties of many genetic oscillator models, we provide an easy-verified sufficient condition for the stochastic synchronization of coupled genetic oscillators, based on the Lur'e system approach in control theory. A design principle for minimizing the influence of noise is also presented. To demonstrate the effectiveness of our theoretical results, a population of coupled repressillators is adopted as a numerical example.
  
  
  CONCLUSIONS
  In summary, we present an efficient theoretical method for analyzing the synchronization of genetic oscillator networks, which is helpful for understanding and testing the synchronization phenomena in biological organisms. Besides, the results are actually applicable to general oscillator networks.
authors.affiliation: Centre for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China. cgli@uestc.edu.cn
authors.forename: Chunguang
authors.initials: C
authors.lastname: Li
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
mesh_terms: D001683:Biological Clocks / Q000235:genetics*; D003198:Computer Simulation / Q000706:statistics & numerical data*; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D013269:Stochastic Processes