On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
An Introduction to Metabolic Networks and Their Structural Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Not all scale free networks are Born equal: the role of the seed graph in PPI network emulation
RECOMB'06 Proceedings of the joint 2006 satellite conference on Systems biology and computational proteomics
An important connection between network motifs and parsimony models
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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Recent studies of properties of various biological networks revealed that many of them display scale-free characteristics. Since the theory of scale-free networks is applicable to evolving networks, one can hope that it provides not only a model of a biological network in its current state but also sheds some insight into the evolution of the network. In this work, we investigate the probability distributions and scaling properties underlying some models for biological networks and protein domain evolution. The analysis of evolutionary models for domain similarity networks indicates that models which include evolutionary drift are typically not scale free. Instead they adhere quite closely to the Yule distribution. This finding indicates that the direct applicability of scale-free models in understanding the evolution of biological network may not be as wide as it has been hoped for.