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

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dc.creatorTakemoto, Kazuhiro
dc.creatorOosawa, Chikoo
dc.date2006-11-04
dc.date.accessioned2026-07-07T08:21:13Z
dc.date.available2026-07-07T08:21:13Z
dc.descriptionWe 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.
dc.description19 pages, 8 figures
dc.identifierhttps://arxiv.org/abs/q-bio/0611014
dc.identifierhttp://arxiv.org/abs/q-bio/0611014
dc.identifierMathematical Biosciences 208, 454 (2007)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/135287
dc.subjectMolecular Networks
dc.subjectDisordered Systems and Neural Networks
dc.titleModeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity
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