Random Boolean Networks have been introduced by Kauffman more than thirty years ago as a highly simplified model of genetic regulatory networks. These models are interesting in their own as complex dynamical systems and have been throughly studied as such. We believe that the original view of Kauffman is still a valid one, provided that the model is updated to take into account present knowledge, without loosing its attractive simplicity. Thus, we will present how the Kauffman model could be modified in order to qualitatively agree with experimental observations that were not available at the time. In particular, we will present a method for generating networks with given degree distributions, together with a new semi-synchronous updating scheme. Simulations of statistical ensembles of networks behaving according to the new model will be presented and discussed
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