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A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes

We present a new theoretical framework to represent the dynamics of structured populations through time and across geographic space. We show (i) that the mechanisms by which populations evolve lead to combinatorial structures, and (ii) that measures of gene flow and geographical structure lead to li...

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Detalles Bibliográficos
Autores principales: Amaris, Armando J. R., Cox, Murray P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4532729/
https://www.ncbi.nlm.nih.gov/pubmed/25307774
http://dx.doi.org/10.1007/s00285-014-0841-4
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author Amaris, Armando J. R.
Cox, Murray P.
author_facet Amaris, Armando J. R.
Cox, Murray P.
author_sort Amaris, Armando J. R.
collection PubMed
description We present a new theoretical framework to represent the dynamics of structured populations through time and across geographic space. We show (i) that the mechanisms by which populations evolve lead to combinatorial structures, and (ii) that measures of gene flow and geographical structure lead to linear systems. These characteristics determine two polytope complexes that encodes all feasible migration scenarios. Analysis of these polytope complexes demonstrates how systems of structured populations can be classified consistently, and how population histories can be represented as paths on a concrete mathematical space, which in turn promises to simplify the search space required for reconstructing past migration processes from population genetic data.
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spelling pubmed-45327292015-08-15 A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes Amaris, Armando J. R. Cox, Murray P. J Math Biol Article We present a new theoretical framework to represent the dynamics of structured populations through time and across geographic space. We show (i) that the mechanisms by which populations evolve lead to combinatorial structures, and (ii) that measures of gene flow and geographical structure lead to linear systems. These characteristics determine two polytope complexes that encodes all feasible migration scenarios. Analysis of these polytope complexes demonstrates how systems of structured populations can be classified consistently, and how population histories can be represented as paths on a concrete mathematical space, which in turn promises to simplify the search space required for reconstructing past migration processes from population genetic data. Springer Berlin Heidelberg 2014-10-12 2015 /pmc/articles/PMC4532729/ /pubmed/25307774 http://dx.doi.org/10.1007/s00285-014-0841-4 Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Article
Amaris, Armando J. R.
Cox, Murray P.
A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes
title A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes
title_full A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes
title_fullStr A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes
title_full_unstemmed A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes
title_short A flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes
title_sort flexible theoretical representation for the temporal dynamics of structured populations as paths on polytope complexes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4532729/
https://www.ncbi.nlm.nih.gov/pubmed/25307774
http://dx.doi.org/10.1007/s00285-014-0841-4
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