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Survival of the sparsest: robust gene networks are parsimonious

Biological gene networks appear to be dynamically robust to mutation, stochasticity, and changes in the environment and also appear to be sparsely connected. Studies with computational models, however, have suggested that denser gene networks evolve to be more dynamically robust than sparser network...

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Autor principal: Leclerc, Robert D
Formato: Texto
Lenguaje:English
Publicado: Nature Publishing Group 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2538912/
https://www.ncbi.nlm.nih.gov/pubmed/18682703
http://dx.doi.org/10.1038/msb.2008.52
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author Leclerc, Robert D
author_facet Leclerc, Robert D
author_sort Leclerc, Robert D
collection PubMed
description Biological gene networks appear to be dynamically robust to mutation, stochasticity, and changes in the environment and also appear to be sparsely connected. Studies with computational models, however, have suggested that denser gene networks evolve to be more dynamically robust than sparser networks. We resolve this discrepancy by showing that misassumptions about how to measure robustness in artificial networks have inadvertently discounted the costs of network complexity. We show that when the costs of complexity are taken into account, that robustness implies a parsimonious network structure that is sparsely connected and not unnecessarily complex; and that selection will favor sparse networks when network topology is free to evolve. Because a robust system of heredity is necessary for the adaptive evolution of complex phenotypes, the maintenance of frugal network complexity is likely a crucial design constraint that underlies biological organization.
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spelling pubmed-25389122008-09-17 Survival of the sparsest: robust gene networks are parsimonious Leclerc, Robert D Mol Syst Biol Report Biological gene networks appear to be dynamically robust to mutation, stochasticity, and changes in the environment and also appear to be sparsely connected. Studies with computational models, however, have suggested that denser gene networks evolve to be more dynamically robust than sparser networks. We resolve this discrepancy by showing that misassumptions about how to measure robustness in artificial networks have inadvertently discounted the costs of network complexity. We show that when the costs of complexity are taken into account, that robustness implies a parsimonious network structure that is sparsely connected and not unnecessarily complex; and that selection will favor sparse networks when network topology is free to evolve. Because a robust system of heredity is necessary for the adaptive evolution of complex phenotypes, the maintenance of frugal network complexity is likely a crucial design constraint that underlies biological organization. Nature Publishing Group 2008-08-05 /pmc/articles/PMC2538912/ /pubmed/18682703 http://dx.doi.org/10.1038/msb.2008.52 Text en Copyright © 2008, EMBO and Nature Publishing Group http://creativecommons.org/licenses/by-nc-sa/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.
spellingShingle Report
Leclerc, Robert D
Survival of the sparsest: robust gene networks are parsimonious
title Survival of the sparsest: robust gene networks are parsimonious
title_full Survival of the sparsest: robust gene networks are parsimonious
title_fullStr Survival of the sparsest: robust gene networks are parsimonious
title_full_unstemmed Survival of the sparsest: robust gene networks are parsimonious
title_short Survival of the sparsest: robust gene networks are parsimonious
title_sort survival of the sparsest: robust gene networks are parsimonious
topic Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2538912/
https://www.ncbi.nlm.nih.gov/pubmed/18682703
http://dx.doi.org/10.1038/msb.2008.52
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