Numerical and Theoretical Studies of Noise Effects in the Kauffman Model

Abstract

In this work we analyze the stochastic dynamics of the Kauffman model evolving under the influence of noise. By considering the average crossing time between two distinct trajectories, we show that different Kauffman models exhibit a similar kind of behavior, even when the structure of their basins of attraction is quite different. This can be considered as a robust property of these models. We present numerical results for the full range of noise level and obtain approximate analytic expressions for the above crossing time as a function of the noise in the limit cases of small and large noise levels.

24 pages, 9 figures, Submitted to the Journal of Statistical Physics

References in corpus (1)

• Stability of the Kauffman Model

Cited by in corpus (10)

• Stable and unstable attractors in Boolean networks
• Stochastic Model of Yeast Cell Cycle Network
• Canalization and Symmetry in Boolean Models for Genetic Regulatory Networks
• Noise in random Boolean networks
• Redundancy and error resilience in Boolean Networks
• Boolean networks with robust and reliable trajectories
• Response of Boolean networks to perturbations
• The computational complexity of Kauffman nets and the P versus NP problem
• The behavior of noise-resilient Boolean networks with diverse topologies
• Circuits, Attractors and Reachability in Mixed-K Kauffman Networks