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

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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.
Original languageEnglish
Title of host publicationGenetic Programming
Subtitle of host publicationEuroGP 2018
PublisherSpringer
Pages151-165
Number of pages15
ISBN (Electronic)9783319775531
ISBN (Print)9783319775524
DOIs
Publication statusPublished - 2 Mar 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10781
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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    Taou, N. S., & Lones, M. A. (2018). Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States. In Genetic Programming: EuroGP 2018 (pp. 151-165). (Lecture Notes in Computer Science; Vol. 10781). Springer. https://doi.org/10.1007/978-3-319-77553-1_10