Computational Science Technical Note CSTN-039

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Simulating Large Random Boolean Networks

K. A. Hawick, H. A. James and C. J. Scogings

Archived May 2007

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.

Keywords: random boolean network; time series analysis; high memory; simulation.

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@TECHREPORT{CSTN-039,
  author = {K. A. Hawick and H. A. James and C. J. Scogings},
  title = {Simulating Large Random Boolean Networks},
  institution = {Computer Science, Massey University},
  year = {2007},
  number = {CSTN-039},
  address = {Albany, North Shore 102-904, Auckland, New Zealand},
  month = {May},
  timestamp = {2007.07.12},
  url = {http://www.massey.ac.nz/~kahawick/cstn/039/cstn-039.pdf}
}


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