Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution

Autor: Tadamune Kaneko, Macoto Kikuchi
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Evolutionary Genetics
Computer and Information Sciences
Evolutionary Processes
QH301-705.5
Entropy
Molecular Networks (q-bio.MN)
FOS: Physical sciences
Mycology
Network Motifs
Evolution
Molecular
Cellular and Molecular Neuroscience
Fungal Evolution
Genetics
Quantitative Biology::Populations and Evolution
Quantitative Biology - Molecular Networks
Evolutionary Emergence
Computer Simulation
Gene Regulatory Networks
Physics - Biological Physics
Selection
Genetic
Biology (General)
Molecular Biology
Condensed Matter - Statistical Mechanics
Ecology
Evolution
Behavior and Systematics
Evolutionary Biology
Models
Genetic
Statistical Mechanics (cond-mat.stat-mech)
Ecology
Physics
Quantitative Biology::Molecular Networks
Biology and Life Sciences
Computational Biology
Probability Theory
Probability Distribution
Quantitative Biology::Genomics
Organismal Evolution
Phenotype
Computational Theory and Mathematics
Biological Physics (physics.bio-ph)
FOS: Biological sciences
Modeling and Simulation
Physical Sciences
Mutation
Thermodynamics
Genetic Fitness
Monte Carlo Method
Network Analysis
Mathematics
Research Article
Zdroj: PLoS Computational Biology, Vol 18, Iss 1, p e1009796 (2022)
PLoS Computational Biology
ISSN: 1553-7358
Popis: The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have developed their functions through evolutionary processes. To understand the particularities of this process theoretically, evolutionary simulation (ES) alone is insufficient because the outcomes of ES depend on evolutionary pathways. We need a reference system for comparison. An appropriate reference system for this purpose is an ensemble of the randomly sampled genotypes. However, generating high-fitness genotypes by simple random sampling is difficult because such genotypes are rare. In this study, we used the multicanonical Monte Carlo method developed in statistical physics to construct a reference ensemble of GRNs and compared it with the outcomes of ES. We obtained the following results. First, mutational robustness was significantly higher in ES than in the reference ensemble at the same fitness level. Second, the emergence of a new phenotype, bistability, was delayed in evolution. Third, the bistable group of GRNs contains many mutationally fragile GRNs compared with those in the non-bistable group. This suggests that the delayed emergence of bistability is a consequence of the mutation-selection mechanism.
Comment: 14 pages, 12 figures
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje