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A Family of Fitness Landscapes Modeled through Gene Regulatory Networks
Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographic...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141513/ https://www.ncbi.nlm.nih.gov/pubmed/35626507 http://dx.doi.org/10.3390/e24050622 |
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author | Yang, Chia-Hung Scarpino, Samuel V. |
author_facet | Yang, Chia-Hung Scarpino, Samuel V. |
author_sort | Yang, Chia-Hung |
collection | PubMed |
description | Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and “ruggedness”. As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population’s evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies. |
format | Online Article Text |
id | pubmed-9141513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91415132022-05-28 A Family of Fitness Landscapes Modeled through Gene Regulatory Networks Yang, Chia-Hung Scarpino, Samuel V. Entropy (Basel) Article Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and “ruggedness”. As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population’s evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies. MDPI 2022-04-29 /pmc/articles/PMC9141513/ /pubmed/35626507 http://dx.doi.org/10.3390/e24050622 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Chia-Hung Scarpino, Samuel V. A Family of Fitness Landscapes Modeled through Gene Regulatory Networks |
title | A Family of Fitness Landscapes Modeled through Gene Regulatory Networks |
title_full | A Family of Fitness Landscapes Modeled through Gene Regulatory Networks |
title_fullStr | A Family of Fitness Landscapes Modeled through Gene Regulatory Networks |
title_full_unstemmed | A Family of Fitness Landscapes Modeled through Gene Regulatory Networks |
title_short | A Family of Fitness Landscapes Modeled through Gene Regulatory Networks |
title_sort | family of fitness landscapes modeled through gene regulatory networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141513/ https://www.ncbi.nlm.nih.gov/pubmed/35626507 http://dx.doi.org/10.3390/e24050622 |
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