Static and dynamic models of biological networks

Authors


  • This paper was submitted as an invited paper resulting from the “Understanding Complex Systems” conference held at the University of Illinois–Urbana Champaign, May 2005

Abstract

We consider static and dynamic approaches to the specification of probability distributions on graphs, consistent with desired statistical properties such as degree distributions, for use in modeling biological networks. In the static approach we develop analytical approximations to the Hamiltonian and partition functions. In the dynamic approach, we use a stochastic parameterized grammar to construct an evolutionary tree in which the nodes represent elements such as genes or cells and the links represent inheritance relations between the nodes. The grammar then constructs a network based on the feature vectors of the nodes in the tree. © 2006 Wiley Periodicals, Inc. Complexity 11:57–63, 2006

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