Dynamics of Gene Regulation on Content-Based Networks

The content-based network model which we have proposed[2] offers possibilities to investigate processes based on molecular recognition and binding. In particular, it seems to be a promising model of gene regulation in its different respects such as topology and dynamics. Random Boolean Dynamics on our content-based network will be introduced and our results will be presented. The network has a scaling behaviour which is different from the scale-free networks investigated up to now.[5] The clustering coefficient and the degree distribution depend on the distribution of the recognition specificities required for binding, which can be tuned at will. The model exhibits a dynamical behaviour which is more ordered or close to critical than its scale-free counterparts. It also opens the way for investigating evolutionary pathways which could have led to existing biological networks.