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 -