Bioinformatics Advance Access originally published online on July 20, 2007
Bioinformatics 2007 23(19):2604-2611; doi:10.1093/bioinformatics/btm370
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Exploring biological network structure using exponential random graph models
Department of Computer Science, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: The functioning of biological networks depends in large part on their complex underlying structure. When studying their systemic nature many modeling approaches focus on identifying simple, but prominent, structural components, as such components are easier to understand, and, once identified, can be used as building blocks to succinctly describe the network.
Results: In recent social network studies, exponential random graph models have been used extensively to model global social network structure as a function of their ‘local features’. Starting from those studies, we describe the exponential random graph models and demonstrate their utility in modeling the architecture of biological networks as a function of the prominence of local features. We argue that the flexibility, in terms of the number of available local feature choices, and scalability, in terms of the network sizes, make this approach ideal for statistical modeling of biological networks. We illustrate the modeling on both genetic and metabolic networks and provide a novel way of classifying biological networks based on the prevalence of their local features.
Contact: saul{at}cs.ucdavis.edu
Received on November 27, 2006; revised on June 6, 2007; accepted on July 11, 2007
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