Towards Intelligent Biological Control: Controlling Boolean Networks with Boolean Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@InProceedings{taou2016towards,
  title =        "Towards Intelligent Biological Control: Controlling
                 {Boolean} Networks with {Boolean} Networks",
  author =       "Nadia S. Taou and David W. Corne and 
                 Michael A. Lones",
  booktitle =    "19th European Conference on Applications of
                 Evolutionary Computation, EvoApplications 2016, Part
                 I",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  volume =       "9597",
  series =       "Lecture Notes in Computer Science",
  pages =        "351--362",
  address =      "Porto, Portugal",
  month =        mar # " 30 -- " # apr # " 1",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Gene
                 regulatory networks, Boolean networks, Control,
                 Evolutionary algorithms",
  isbn13 =       "978-3-319-31204-0",
  DOI =          "doi:10.1007/978-3-319-31204-0_23",
  abstract =     "Gene regulatory networks (GRNs) are the complex
                 dynamical systems that orchestrate the activities of
                 biological cells. In order to design effective
                 therapeutic interventions for diseases such as cancer,
                 there is a need to control GRNs in more sophisticated
                 ways. Computational control methods offer the potential
                 for discovering such interventions, but the difficulty
                 of the control problem means that current methods can
                 only be applied to GRNs that are either very small or
                 that are topologically restricted. In this paper, we
                 consider an alternative approach that uses evolutionary
                 algorithms to design GRNs that can control other GRNs.
                 This is motivated by previous work showing that
                 computational models of GRNs can express complex
                 control behaviours in a relatively compact fashion. As
                 a first step towards this goal, we consider abstract
                 Boolean network models of GRNs, demonstrating that
                 Boolean networks can be evolved to control trajectories
                 within other Boolean networks. The Boolean approach
                 also has the advantage of a relatively easy mapping to
                 synthetic biology implementations, offering a potential
                 path to in vivo realisation of evolved controllers.",
  notes =        "EvoApplications2016 held inconjunction with
                 EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",
}

Genetic Programming entries for Nadia Solime Taou David W Corne Michael A Lones

Citations