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Lethality and entropy of protein interaction networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50420

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50133

Demetrius,  Lloyd
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, 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;

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IBSB05F018.pdf
(beliebiger Volltext), 671KB

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Zitation

Manke, T., Demetrius, L., & Vingron, M. (2005). Lethality and entropy of protein interaction networks. In R. Heinrich, C. DeLisi, M. Kanehisa, & a. S. Miyano (Eds.), Genome informatics series: proceedings of the.. Workshop on Genome Informatics. Workshop on Genome Informatics (pp. 159-163). Tokyo: Universal Academy Press.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-85D2-F
Zusammenfassung
We characterize protein interaction networks in terms of network entropy. This approach suggests a ranking principle, which strongly correlates with elements of functional importance, such as lethal proteins. Our combined analysis of protein interaction networks and functional profiles in single cellular yeast and multi-cellular worm shows that proteins with large contribution to network entropy are preferentially lethal. While entropy is inherently a dynamical concept, the present analysis incorporates only structural information. Our result therefore highlights the importance of topological features, which appear as correlates of an underlying dynamical property, and which in turn determine functional traits. We argue that network entropy is a natural extension of previously studied observables, such as pathway multiplicity and centrality. It is also applicable to networks in which the processes can be quantified and therefore serves as a link to study questions of structural and dynamical robustness in a unified way.