Advertisement

An Efficient Simulator for Boolean Network Models

  • Stefano BenedettiniEmail author
  • Andrea Roli
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Boolean networks (BNs), first introduced by Kauffman as genetic regulatory network models, are the subject of notable works in complex systems biology literature. BN models lately garnered much attention because it has been shown that BNs can capture important phenomena in genetics and biology in general. In this work, we illustrate the main properties and design principles of a new efficient, flexible and extensible BN simulator, named the Boolean Network Toolkit. This simulator makes it possible to easily set up experiments and analyse the most relevant features of BN’s dynamics.

Keywords

Boolean Network Genetic Regulatory Network Efficient Simulator Stochastic Local Search Large Size System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Kauffman S (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, London Google Scholar
  2. 2.
    Kauffman S (2004) A proposal for using the ensemble approach to understand genetic regulatory networks. J Theor Biol 230:581–590 MathSciNetCrossRefGoogle Scholar
  3. 3.
    Benedettini S, Roli A, Serra R, Villani M (2011) Stochastic local search to automatically design Boolean networks with maximally distant attractors. In: Di Chio C, Cagnoni S, Cotta C, Ebner M, Ekárt A, Esparcia-Alcázar A, Merelo J, Neri F, Preuss M, Richter H, Togelius J, Yannakakis G (eds) Applications of evolutionary computation. Lecture notes in computer science. Springer, Heidelberg, pp 22–31 CrossRefGoogle Scholar
  4. 4.
    Benedettini S The Boolean network toolkit. http://sourceforge.net/projects/booleannetwork/
  5. 5.
    Serra R, Villani M, Barbieri A, Kauffman S, Colacci A (2010) On the dynamics of random Boolean networks subject to noise: attractors, ergodic sets and cell types. J Theor Biol 265(2):185–193 MathSciNetCrossRefGoogle Scholar
  6. 6.
    Siek JG, Lee LQ, Lumsdaine A (2002) The Boost graph library: user guide and reference manual. Addison-Wesley, Reading Google Scholar
  7. 7.
    Brent RP (1980) An improved Monte Carlo factorization algorithm. BIT Numer Math 20:176–184 MathSciNetADSzbMATHCrossRefGoogle Scholar
  8. 8.
    Knuth D (1998) The art of computer programming. Volume 2: Seminumerical algorithms, 3rd edn. Pearson Education, Upper Saddle River Google Scholar
  9. 9.
    Derrida B, Pomeau Y (1986) Random networks of automata: a simple annealed approximation. Europhys Lett 1(2):45–49 ADSCrossRefGoogle Scholar
  10. 10.
    Heckel R, Schober S, Bossert M (2010) On random boolean threshold networks. In: International ITG conference on source and channel coding (SCC), pp 1–6 Google Scholar
  11. 11.
    Kappler K, Edwards R, Glass L (2003) Dynamics in high-dimensional model gene networks. Signal Process 83:789–798 zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.European Centre for Living TechnologyVeniceItaly
  2. 2.DEIS-Cesena, Alma Mater StudiorumUniversity of BolognaBolognaItaly

Personalised recommendations