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

Abstract

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

References in corpus (17)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Assortative mixing in networks
• Hierarchical organization of modularity in metabolic networks
• Mean-field theory for scale-free random networks
• Hierarchical Organization in Complex Networks
• Dynamical and correlation properties of the Internet
• Weighted evolving networks: coupling topology and weights dynamics
• Modeling the evolution of weighted networks
• Preferential attachment in the protein network evolution
• Scale-free Networks on Lattices
• The topology of the transcription regulatory network in the yeast, S. cerevisiae
• The effect of aging on network structure
• Rate equation approach for correlations in growing network models
• Self-similar Scale-free Networks and Disassortativity
• Evolving networks by merging cliques
• Graph theoretic analysis of protein interaction networks of eukaryotes