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European Conference on Genetic Programming

EuroGP 2018: Genetic Programming pp 151–165Cite as

Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

Abstract

Within the genetic programming community, there has been growing interest in the use of computational representations motivated by gene regulatory networks (GRNs). It is thought that these representations capture useful biological properties, such as evolvability and robustness, and thereby support the evolution of complex computational behaviours. However, computational evolution of GRNs also opens up opportunities to go in the opposite direction: designing programs that could one day be implemented in biological cells. In this paper, we explore the ability of evolutionary algorithms to design Boolean networks, abstract models of GRNs suitable for refining into synthetic biology implementations, and show how they can be used to control cell states within a range of executable models of biological systems.

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Authors and Affiliations

  1. School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK

    Nadia S. Taou & Michael A. Lones

Authors
  1. Nadia S. Taou
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  2. Michael A. Lones
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Corresponding author

Correspondence to Michael A. Lones .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

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Taou, N.S., Lones, M.A. (2018). Towards in Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-77553-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77552-4

  • Online ISBN: 978-3-319-77553-1

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