Evolved Artificial Signalling Networks for the Control of a Conservative Complex Dynamical System

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

Abstract

Artificial Signalling Networks (ASNs) are computational models inspired by cellular signalling processes that interpret environmental information. This paper introduces an ASN-based approach to controlling chaotic dynamics in discrete dynamical systems, which are representative of complex behaviours which occur in the real world. Considering the main biological interpretations of signalling pathways, two ASN models are developed. They highlight how pathways’ complex behavioural dynamics can be captured and represented within evolutionary algorithms. In addition, the regulatory capacity of the major regulatory functions within living organisms is also explored. The results highlight the importance of the representation to model signalling pathway behaviours and reveal that the inclusion of crosstalk positively affects the performance of the model.

Keywords

Chaotic Dynamic Signalling Network Interaction Graph Discrete Dynamical System Evolution Rule 
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

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

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