Abstract
Protein-protein interaction networks, particularly that of the yeast S. Cerevisiae, have recently been studied extensively. These networks seem to satisfy the small world property and their (1-hop) degree distribution seems to form a power law. More recently, a number of duplication based random graph models have been proposed with the aim of emulating the evolution of protein-protein interaction networks and satisfying these two graph theoretical properties. In this paper, we show that the proposed model of Pastor-Satorras et al. does not generate the power law degree distribution with exponential cutoff as claimed and the more restrictive model by Chung et al. cannot be interpreted unconditionally. It is possible to slightly modify these models to ensure that they generate a power law degree distribution. However, even after this modification, the more general k-hop degree distribution achieved by these models, for k > 1, are very different from that of the yeast proteome network. We address this problem by introducing a new network growth model that takes into account the sequence similarity between pairs of proteins (as a binary relationship) as well as their interactions. The new model captures not only the k-hop degree distribution of the yeast protein interaction network for all k > 0, but it also captures the 1-hop degree distribution of the sequence similarity network, which again seems to form a power law.
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Bebek, G., Berenbrink, P., Cooper, C., Friedetzky, T., Nadeau, J.H., Sahinalp, S.C. (2007). Improved Duplication Models for Proteome Network Evolution. In: Eskin, E., Ideker, T., Raphael, B., Workman, C. (eds) Systems Biology and Regulatory Genomics. RSB RRG 2005 2005. Lecture Notes in Computer Science(), vol 4023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48540-7_11
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DOI: https://doi.org/10.1007/978-3-540-48540-7_11
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