Biology – Quantitative Biology – Quantitative Methods
Scientific paper
2007-04-27
Phys. Rev. E 75, 051910 (2007)
Biology
Quantitative Biology
Quantitative Methods
16 pages, 3 figures, in Press PRE uses pdflatex
Scientific paper
10.1103/PhysRevE.75.051910
The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k of other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free-energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about two. This shows that degree distributions are not sufficient to determine the properties of PINs.
Bomsztyk Karol
Miller Gerald A.
Qian Hong
Shi Yi Y.
No associations
LandOfFree
Clustering Coefficients of Protein-Protein Interaction Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Clustering Coefficients of Protein-Protein Interaction Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Clustering Coefficients of Protein-Protein Interaction Networks will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-367270