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Design of robust flow processing networks with time-programmed responses
We design flow processing networks with time-programmed responses. This network model allows us to consider situations when activation of an input node triggers a coordinated series of responses in different output nodes, generating a specific temporal pattern. A network is not only designed to be functional performing a target response, it is also optimized to be robust against local damages as the deletion of one its nodes or one of its links. We show that the design of robust functional networks is possible by applying evolutionary optimization algorithms of mutations and selections. We present the main properties of these networks, including their motif distributions, as function of the different robust design criteria. Finally, we present the case of steady flows for this model and its relationship with previously considered static flow distribution network model [1,2].
 Kaluza, P., M. Ipsen, M. Vingron, and A. S. Mikhailov. Design and statistical properties of robust functional networks: A model study of biological signal transduction. Phys. Rev. E. 75, 015101 (2007)
 Kaluza, P., M. Vingron, and A. S. Mikhailov. Self-correcting networks: Function, robustness, and motif distributions in biological signal processing. Chaos 18, 026113 (2008)