TY - GEN
T1 - Boolean Threshold Networks as Models of Genotype-Phenotype Maps
AU - Camargo, Chico Q.
AU - Louis, Ard A.
N1 - Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Boolean threshold networks (BTNs) are a class of mathematical models used to describe complex dynamics on networks. They have been used to study gene regulation, but also to model the brain, and are similar to artificial neural networks used in machine learning applications. In this paper we study BTNs from the perspective of genotype-phenotype maps, by treating the network’s set of nodes and connections as its genotype, and dynamic behaviour of the model as its phenotype. We show that these systems exhibit (1) Redundancy, that is many genotypes map to the same phenotypes; (2) Bias, the number of genotypes per phenotypes varies over many orders of magnitude; (3) Simplicity bias, simpler phenotypes are exponentially more likely to occur than complex ones; (4) Large robustness, many phenotypes are surprisingly robust to random perturbations in the parameters, and (5) this robustness correlates positively with the evolvability, the ability of the system to find other phenotypes by point mutations of the parameters. These properties should be relevant for the wide range of systems that can be modelled by BTNs.
AB - Boolean threshold networks (BTNs) are a class of mathematical models used to describe complex dynamics on networks. They have been used to study gene regulation, but also to model the brain, and are similar to artificial neural networks used in machine learning applications. In this paper we study BTNs from the perspective of genotype-phenotype maps, by treating the network’s set of nodes and connections as its genotype, and dynamic behaviour of the model as its phenotype. We show that these systems exhibit (1) Redundancy, that is many genotypes map to the same phenotypes; (2) Bias, the number of genotypes per phenotypes varies over many orders of magnitude; (3) Simplicity bias, simpler phenotypes are exponentially more likely to occur than complex ones; (4) Large robustness, many phenotypes are surprisingly robust to random perturbations in the parameters, and (5) this robustness correlates positively with the evolvability, the ability of the system to find other phenotypes by point mutations of the parameters. These properties should be relevant for the wide range of systems that can be modelled by BTNs.
KW - Boolean networks
KW - Gene regulatory networks
KW - Genotype-phenotype maps
KW - Input-output maps
UR - http://www.scopus.com/inward/record.url?scp=85081328310&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-40943-2_13
DO - 10.1007/978-3-030-40943-2_13
M3 - Conference contribution
AN - SCOPUS:85081328310
SN - 9783030409425
T3 - Springer Proceedings in Complexity
SP - 143
EP - 155
BT - Complex Networks XI - Proceedings of the 11th Conference on Complex Networks, CompleNet 2020
A2 - Barbosa, Hugo
A2 - Menezes, Ronaldo
A2 - Gomez-Gardenes, Jesus
A2 - Gonçalves, Bruno
A2 - Mangioni, Giuseppe
A2 - Oliveira, Marcos
PB - Springer
T2 - 11th International Conference on Complex Networks, CompleNet 2020
Y2 - 31 March 2020 through 3 April 2020
ER -