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Using Artificial Epigenetic Regulatory Networks to Control Complex Tasks within Chaotic Systems

  • Alexander P. Turner
  • Michael A. Lones
  • Luis A. Fuente
  • Susan Stepney
  • Leo S. Caves
  • Andy M. Tyrrell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7223)

Abstract

Artificial gene regulatory networks are computational models which draw inspiration from real world networks of biological gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper introduces a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. The results demonstrate that the AERNs are more adept at controlling multiple opposing trajectories within Chirikov’s standard map, suggesting that AERNs are an interesting area for further investigation.

Keywords

Regulatory Network Chaotic Dynamic Epigenetic Mechanism Gene Regulatory Network Real World Network 
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 2012

Authors and Affiliations

  • Alexander P. Turner
    • 1
    • 4
  • Michael A. Lones
    • 1
    • 4
  • Luis A. Fuente
    • 1
    • 4
  • Susan Stepney
    • 2
    • 4
  • Leo S. Caves
    • 3
    • 4
  • Andy M. Tyrrell
    • 1
    • 4
  1. 1.Department of ElectronicsUniversity of YorkHeslingtonUK
  2. 2.Department of Computer ScienceUniversity of YorkHeslingtonUK
  3. 3.Department of BiologyUniversity of YorkHeslingtonUK
  4. 4.York Centre for Complex Systems Analysis (YCCSA)University of YorkHeslingtonUK

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