Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

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We propose a growing network model that consists of two tunable mechanisms: growth by merging modules which are represented as complete graphs and a fitness-driven preferential attachment. Our model exhibits the three prominent statistical properties are widely shared in real biological networks, for example gene regulatory, protein-protein interaction, and metabolic networks. They retain three power law relationships, such as the power laws of degree distribution, clustering spectrum, and degree-degree correlation corresponding to scale-free connectivity, hierarchical modularity, and disassortativity, respectively. After making comparisons of these properties between model networks and biological networks, we confirmed that our model has inference potential for evolutionary processes of biological networks.
19 pages, 8 figures

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Molecular Networks, Disordered Systems and Neural Networks

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http://salesiana.dossiersoluciones.com/handle/123456789/135287

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/q-bio/0611014
 http://arxiv.org/abs/q-bio/0611014
 Mathematical Biosciences 208, 454 (2007)

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arXiv