The Robustness of Balanced Boolean Networks

  • Ming Liu
  • Elena Dubrova
Part of the Studies in Computational Intelligence book series (SCI, volume 424)


One of the characteristic features of genetic regulatory networks is their inherent robustness, that is, their ability to retain functionality in spite of the introduction of random errors. In this paper, we focus on the robustness of Balanced Boolean Networks (BBNs), which is a special kind of Boolean Network model of genetic regulatory networks. Our goal is to formalize and analyse the robustness of BBNs. Based on these results, applications using Boolean network model can be improved and optimized to be more robust.

We formalize BBNs and introduce a method to construct BBNs for 2-singleton attractors Boolean networks. The experiment results show that BBNs have a good performance on tolerating the single stuck-at faults on every edge. Our method improves the robustness of Boolean networks by at least 13% in average, and in some special case, up to 61%.


Boolean Function Boolean Network Genetic Regulatory Network State Transition Graph Boolean Space 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Information and Communication TechnologyRoyal Institute of Technology(KTH)StockholmSweden

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