Mean-field model of genetic regulatory networks
Mean-field model of genetic regulatory networks
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DOI：
10.1088/1367-2630/8/8/148
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发表时间：
2006-08-25
影响因子：
3.3
通讯作者：
Kauffman, S. A.
中科院分区：
文献类型：
--
作者：
Andrecut, M.;Kauffman, S. A.
在本文中，我们提出了一个平均场模型，它试图在随机布尔网络和更现实的遗传调控网络随机建模之间架起一座桥梁。该模型的主要思想是用一个平均或有效的相互作用来取代任何一个基因的所有调控相互作用，这考虑到了抑制和激活机制。我们发现，依赖于调节参数的集合，模型表现出丰富的非线性动力学。该模型也为随机布尔网络的早期定性结果提供了定量支持。
In this paper, we propose a mean-field model which attempts to bridge the gap between random Boolean networks and more realistic stochastic modelling 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.