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

MPS-Authors

Kaluza,  Pablo
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50613

Vingron,  Martin
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

Mikhailov,  Alexander S.
Max Planck Society;

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Citation

Kaluza, P., Vingron, M., & Mikhailov, A. S. (2008). Self-correcting networks: Function, robustness, and motif distributions in biological signal processing. Chaos: an Interdisciplinary Journal of Nonlinear Sciencc, 18(2), 026113-026113. doi:10.1063/1.2945228.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-7FA5-2
Abstract
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. We find that robustness against noise and random local damage plays a dominant role in determining motif distributions of networks and may underlie their classification into network superfamilies