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

  • Nadia S. Taou
  • Michael A. LonesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10781)


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.


Gene regulatory networks Boolean networks Control Evolutionary algorithms 


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematical and Computer SciencesHeriot-Watt UniversityEdinburghUK

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