Seeing the net for the nodes: Coarse-graining modular Boolean networks

We propose a novel method to reduce the dimensionality of modular Boolean networks, in which an original network is represented by a coarse-grained representation. We analyzed the performance of the method by simulations of randomly wired modular Kauffman and activator-inhibitor networks with respect to reliability, determinism, and efficiency. It is shown that the reliability of the method depends on the separation of time scales between intermodular and intermodular dynamics, and that this requirement is more likely fulfilled for activator-inhibitor networks than for Kauffman networks. If the requirement is fulfilled, the proposed coarse-graining method produces correct and mainly deterministic projections with high dimensional reduction.

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