Graudenzi Alex, Serra Roberto, Villani Marco, Damiani Chiara, Colacci Annamaria, Kauffman Stuart A
European Centre for Living Technology (ECLT), University Cá Foscari of Venice, Italy.
J Comput Biol. 2011 Oct;18(10):1291-303. doi: 10.1089/cmb.2010.0069. Epub 2011 Jan 7.
Classical random Boolean networks (RBN) are not well suited to describe experimental data from time-course microarray, mainly because of the strict assumptions about the synchronicity of the regulatory mechanisms. In order to overcome this setback, a generalization of the RBN model is described and analyzed. Gene products (e.g., regulatory proteins) are introduced, with each one characterized by a specific decay time, thereby introducing a form of memory in the system. The dynamics of these networks is analyzed, and it is shown that the distribution of the decay times has a strong effect that can be adequately described and understood. The implications for the dynamical criticality of the networks are also discussed.
经典随机布尔网络（RBN）不太适合描述来自时间进程微阵列的实验数据，主要是因为对调控机制同步性的严格假设。为了克服这一挫折，描述并分析了RBN模型的一种推广形式。引入了基因产物（例如调控蛋白），每个基因产物都具有特定的衰减时间，从而在系统中引入了一种记忆形式。分析了这些网络的动力学，结果表明衰减时间的分布具有很强的影响，这种影响可以得到充分的描述和理解。还讨论了这些网络的动力学临界性的相关影响。
