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Design and statistical properties of robust functional networks: A model study of biological signal transduction

Pablo Kaluza, Mads Ipsen, Martin Vingron, and Alexander S. Mikhailov
Phys. Rev. E 75, 015101(R) – Published 19 January 2007


A simple flow network model of biological signal transduction is investigated. Networks with prescribed signal processing functions, robust against random node or link removals, are designed through an evolutionary optimization process. Statistical properties of large ensembles of such networks, including their characteristic motif distributions, are determined. Our analysis suggests that robustness against link removals plays the principal role in the architecture of real signal transduction networks and developmental genetic transcription networks.

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  • Received 10 March 2006


©2007 American Physical Society

Authors & Affiliations

Pablo Kaluza1, Mads Ipsen2, Martin Vingron3, and Alexander S. Mikhailov1

  • 1Department of Physical Chemistry, Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany
  • 2Department of Chemistry, H. C. Ørsted Institute, University of Copenhagen, Universitetsparken 5, Copenhagen Ø2100, Denmark
  • 3Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany

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Vol. 75, Iss. 1 — January 2007

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Physical Review E Scope Description to Include Biological Physics
January 14, 2016

The editors of Physical Review E are pleased to announce that the journal’s stated scope has been expanded to explicitly include the term “Biological Physics.”

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