Evolving Noisy Oscillatory Dynamics in Genetic Regulatory Networks

  • André Leier
  • P. Dwight Kuo
  • Wolfgang Banzhaf
  • Kevin Burrage
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3905)


We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.


Genetic Network Genetic Regulatory Network Catalytic Degradation Stochastic Simulation Algorithm Genetic Circuit 
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 2006

Authors and Affiliations

  • André Leier
    • 1
  • P. Dwight Kuo
    • 2
  • Wolfgang Banzhaf
    • 3
  • Kevin Burrage
    • 1
  1. 1.Advanced Computational Modelling CentreUniversity of QueenslandBrisbaneAustralia
  2. 2.Department of BioengineeringUniversity of CaliforniaSan Diego, La JollaUSA
  3. 3.Dept. of Computer ScienceMemorial University of NewfoundlandSt. John’sCanada

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