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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7FA5-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-7FA6-F
Genre: Journal Article

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Chaos_18_026113.pdf (Any fulltext), 2MB
 
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 Creators:
Kaluza, Pablo1, Author
Vingron, Martin2, Author              
Mikhailov, Alexander S.1, Author
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1Max Planck Society, ou_persistent13              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 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

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Language(s): eng - English
 Dates: 2008-06-27
 Publication Status: Published in print
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Title: Chaos : an Interdisciplinary Journal of Nonlinear Sciencc
Source Genre: Journal
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Pages: - Volume / Issue: 18 (2) Sequence Number: - Start / End Page: 026113 - 026113 Identifier: ISSN: 1054-1500