Simulating large random Boolean networks

dc.contributor.author	Hawick, K.A.
dc.contributor.author	James, H.A.
dc.contributor.author	Scogings, C.J.
dc.date.accessioned	2013-05-20T23:51:14Z
dc.date.available	2013-05-20T23:51:14Z
dc.date.issued	2007
dc.identifier.citation	Hawick, K.A., James, H.A., Scogings, C.J. (2007), Simulating large random Boolean networks, Research Letters in the Information and Mathematical Sciences, 11, 33-43	en
dc.identifier.issn	1175-2777
dc.identifier.uri	http://hdl.handle.net/10179/4493
dc.description.abstract	The 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.language.iso	en	en
dc.publisher	Massey University	en
dc.subject	Random Boolean network	en
dc.subject	Time series analysis	en
dc.subject	Networks	en
dc.subject	Complex systems	en
dc.title	Simulating large random Boolean networks	en
dc.type	Article	en