TY - GEN
T1 - Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States
AU - Taou, Nadia Solime
AU - Lones, Michael Adam
PY - 2018/3/2
Y1 - 2018/3/2
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-77553-1_10
DO - 10.1007/978-3-319-77553-1_10
M3 - Conference contribution
SN - 9783319775524
T3 - Lecture Notes in Computer Science
SP - 151
EP - 165
BT - Genetic Programming
PB - Springer
ER -