Dec 17, 2009

Exploring biological network structure with clustered random networks

BMC Bioinformatics
Shweta BansalLauren Ancel Meyers

Abstract

Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our ...Continue Reading

  • References38
  • Citations28

References

  • References38
  • Citations28

Citations

Mentioned in this Paper

Calcinus elegans
Metabolic Process, Cellular
Cyartonema elegans
Coleonyx elegans
Cestrum elegans
Clarkia unguiculata
Clathrulina elegans
Cardioglossa elegans
Cymbella elegans
Cyrenella elegans

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