Random Boolean network models and the yeast transcriptional network
我们最近通过用一个简化的布尔网络模型来分析酵母的转录网络。期望在生成的布尔网络的稳定条件下，找出结构中可行的规则。我们发现在所有生成的模型中，那些canalyzing布尔规则的模型特别稳定，而那些随机布尔规则的模型只在边缘稳定。另外，生成的网络的的实质性部分是不变的，也就是说不论他们的初始状态怎样，最终它们总是到达同样的状态。所以，我们总的方法提示酵母的转录网络显示了高度的动力学规则性。
Stuart Kauffman*, Carsten Peterson唶, Bjo?rn Samuelsson? and Carl Troein?
*Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131; and 咰omplex Systems Division,
Department of Theoretical Physics, Lund University, So?lvegatan 14A, S-223 62 Lund, Sweden
Communicated by Philip W. Anderson, Princeton University, Princeton, NJ, October 6, 2003 (received for review June 30, 2003)
The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that, for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state, regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.
