Simulating large random Boolean networks

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dc.contributor.authorHawick, K.A.
dc.contributor.authorJames, H.A.
dc.contributor.authorScogings, C.J.
dc.date.accessioned2013-05-20T23:51:14Z
dc.date.available2013-05-20T23:51:14Z
dc.date.issued2007
dc.description.abstractThe Kauffman N-K, or random boolean network, model is an important tool for exploring the properties of large scale complex systems. There are computational challenges in simulating large networks with high connectivities. We describe some high-performance data structures and algorithms for implementing large-scale simulations of the random boolean network model using various storage types provided by the D programming language. We discuss the memory complexity of an optimised simulation code and present some measured properties of large networks.en
dc.identifier.citationHawick, K.A., James, H.A., Scogings, C.J. (2007), Simulating large random Boolean networks, Research Letters in the Information and Mathematical Sciences, 11, 33-43en
dc.identifier.issn1175-2777
dc.identifier.urihttp://hdl.handle.net/10179/4493
dc.language.isoenen
dc.publisherMassey Universityen
dc.subjectRandom Boolean networken
dc.subjectTime series analysisen
dc.subjectNetworksen
dc.subjectComplex systemsen
dc.titleSimulating large random Boolean networksen
dc.typeArticleen
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Research Letters in the Information and Mathematical Sciences