Simulating large random Boolean networks

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Date
2007
DOI
Open Access Location
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Publisher
Massey University
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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.
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Keywords
Random Boolean network, Time series analysis, Networks, Complex systems
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