Self-correcting networks: Function, robustness and motif distributions in biological signal processing

Statistical properties of large ensembles of networks, all designed to have the same functions of signal processing, but robust against different kinds of perturbations, are analyzed. The study is performed for a simple model of flow distribution networks, which can be considered as an abstraction of real biological signal transduction systems. We find that robustness against noise and random local damage can play a dominant role in determining motif distributions of networks and underlie their classification into network superfamilies. [1] P. Kaluza, M. Vingron, A. S. Mikhailov, Chaos 18, 026113 (2008) [2] P. Kaluza, M. Ipsen, M. Vingron, A. S. Mikhailov, Phys. Rev. E 75, 015101 (2007) [3] P. Kaluza, A. S. Mikhailov, Europhys. Lett. 79, 48001 (2007)

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