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Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory

The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary...

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Detalles Bibliográficos
Autores principales: Bapteste, Eric, Huneman, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5972403/
https://www.ncbi.nlm.nih.gov/pubmed/29843714
http://dx.doi.org/10.1186/s12915-018-0531-6
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author Bapteste, Eric
Huneman, Philippe
author_facet Bapteste, Eric
Huneman, Philippe
author_sort Bapteste, Eric
collection PubMed
description The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology.
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spelling pubmed-59724032018-06-05 Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory Bapteste, Eric Huneman, Philippe BMC Biol Opinion The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology. BioMed Central 2018-05-29 /pmc/articles/PMC5972403/ /pubmed/29843714 http://dx.doi.org/10.1186/s12915-018-0531-6 Text en © Bapteste et al. 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Opinion
Bapteste, Eric
Huneman, Philippe
Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory
title Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory
title_full Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory
title_fullStr Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory
title_full_unstemmed Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory
title_short Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory
title_sort towards a dynamic interaction network of life to unify and expand the evolutionary theory
topic Opinion
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5972403/
https://www.ncbi.nlm.nih.gov/pubmed/29843714
http://dx.doi.org/10.1186/s12915-018-0531-6
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