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| Random Boolean network (RBN) is an abstract model of gene regulation networks. It is known that RBN exhibits a continuous order-disorder phase transition. In recent years, it has been shown that real-world gene regulation networks are working at close to criticality. It has been suggested that optimization of information transfer on gene regulation networks has certain evolutionary advantage because there are evidences that information processing ability of RBN is maximized at criticality. Some authors have been proposed adaptive network models based on simple local rewiring rules and showed that their models evolve toward close to criticality by numerical simulations. However, the role of information transfer in the course of evolution is still unclear in these models because the rewiring rules include no quantities related to information transfer. Here, we propose a new adaptive RBN model whose local rewiring rule involves local information transfer through a single node in the network. Local information transfer is quantified by the local transfer entropy which reflects the quality of information transfer over a single link. We show that our model can evolve toward close to criticality by both numerical simulation and master equation analysis of the in-degree distribution. |
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