Mean Field Model of Genetic Regulatory Networks

Abstract

In this paper, we propose a mean-field model which attempts to bridge the gap between random Boolean networks and more realistic stochastic modeling of genetic regulatory networks. The main idea of the model is to replace all regulatory interactions to any one gene with an average or effective interaction, which takes into account the repression and activation mechanisms. We find that depending on the set of regulatory parameters, the model exhibits rich nonlinear dynamics. The model also provides quantitative support to the earlier qualitative results obtained for random Boolean networks.

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References in corpus (1)

• Enhancement of the stability of genetic switches by overlapping upstream regulatory domains

Cited by in corpus (5)

• Attractors in continuous and Boolean networks
• The window at the edge of chaos in a simple model of gene interaction networks
• Boolean modeling of collective effects in complex networks
• Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics
• Bistability and chaos in the discrete two-gene Andrecut-Kauffman model