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Title: Quantitative Characterization and Scaling of Human Signal Transduction Networks
P189
Ziagos, Sotirios (1); Hoffmann, Martin (2); Wilhelm, Thomas (3)

Sotirios.Ziagos@med.uni-jena.de, mhoffman@pmail.hki-jena.de, wilhelm@imb-jena.de
(1) Friedrich Schiller University, WG Molecular Biology, KIM II, Jena; (2) Hans Knöll Institute for Natural Products Research, Dep. of Applied Microbiology, Jena; (3) Institute for Molecular Biotechnology, JRG Theoretical Systems Biology, Jena;

We study the protein interaction networks underlying signal transduction in human cells [1]. In contrast to previous statements [2], we find that the probability distribution of the number of interactions per protein follows an exponential decay: p(k)~exp(-a k). This means that it is possible to define a characteristic number of connections: k*=1/a.

We demonstrate that protein interaction networks are not randomly connected, but highly clustered. The clustering coefficient [3] is 6.5 times larger than in corresponding random networks.

Furthermore, we study the dependence of our measures (connectivity distribution and clustering coefficient) from the size of the considered network: Starting from one protein in step one all proteins connected to this protein are added, in step two all proteins connected to these proteins are added, and so on. This is done for three different starting proteins: erythropoietin (EP0), phosphatidyl-inositol kinase (Pi3K) and caspase-activated DNase (CAD). It turns out that it is possible to approximate the clustering coefficient and the connectivity distributions with sub-networks of about half the size of the total network. This corresponds to 5 steps in the stepwise expansion of the network and is independent of the protein from which the network expansion was started.
[1] TRANSPATH data base: http://www.transpath.de/
[2] H.Jeong et al., Nature 411,41-41(2001).
[3] D.J.Watts and S.H.Strogatz, Nature 393,440-442(1998).