Cargando…
The geometry of the Pareto front in biological phenotype space
When organisms perform a single task, selection leads to phenotypes that maximize performance at that task. When organisms need to perform multiple tasks, a trade-off arises because no phenotype can optimize all tasks. Recent work addressed this question, and assumed that the performance at each tas...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3686184/ https://www.ncbi.nlm.nih.gov/pubmed/23789060 http://dx.doi.org/10.1002/ece3.528 |
_version_ | 1782273766726828032 |
---|---|
author | Sheftel, Hila Shoval, Oren Mayo, Avi Alon, Uri |
author_facet | Sheftel, Hila Shoval, Oren Mayo, Avi Alon, Uri |
author_sort | Sheftel, Hila |
collection | PubMed |
description | When organisms perform a single task, selection leads to phenotypes that maximize performance at that task. When organisms need to perform multiple tasks, a trade-off arises because no phenotype can optimize all tasks. Recent work addressed this question, and assumed that the performance at each task decays with distance in trait space from the best phenotype at that task. Under this assumption, the best-fitness solutions (termed the Pareto front) lie on simple low-dimensional shapes in trait space: line segments, triangles and other polygons. The vertices of these polygons are specialists at a single task. Here, we generalize this finding, by considering performance functions of general form, not necessarily functions that decay monotonically with distance from their peak. We find that, except for performance functions with highly eccentric contours, simple shapes in phenotype space are still found, but with mildly curving edges instead of straight ones. In a wide range of systems, complex data on multiple quantitative traits, which might be expected to fill a high-dimensional phenotype space, is predicted instead to collapse onto low-dimensional shapes; phenotypes near the vertices of these shapes are predicted to be specialists, and can thus suggest which tasks may be at play. |
format | Online Article Text |
id | pubmed-3686184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-36861842013-06-20 The geometry of the Pareto front in biological phenotype space Sheftel, Hila Shoval, Oren Mayo, Avi Alon, Uri Ecol Evol Original Research When organisms perform a single task, selection leads to phenotypes that maximize performance at that task. When organisms need to perform multiple tasks, a trade-off arises because no phenotype can optimize all tasks. Recent work addressed this question, and assumed that the performance at each task decays with distance in trait space from the best phenotype at that task. Under this assumption, the best-fitness solutions (termed the Pareto front) lie on simple low-dimensional shapes in trait space: line segments, triangles and other polygons. The vertices of these polygons are specialists at a single task. Here, we generalize this finding, by considering performance functions of general form, not necessarily functions that decay monotonically with distance from their peak. We find that, except for performance functions with highly eccentric contours, simple shapes in phenotype space are still found, but with mildly curving edges instead of straight ones. In a wide range of systems, complex data on multiple quantitative traits, which might be expected to fill a high-dimensional phenotype space, is predicted instead to collapse onto low-dimensional shapes; phenotypes near the vertices of these shapes are predicted to be specialists, and can thus suggest which tasks may be at play. Blackwell Publishing Ltd 2013-06 2013-04-17 /pmc/articles/PMC3686184/ /pubmed/23789060 http://dx.doi.org/10.1002/ece3.528 Text en © 2013 Published by John Wiley & Sons Ltd. http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
spellingShingle | Original Research Sheftel, Hila Shoval, Oren Mayo, Avi Alon, Uri The geometry of the Pareto front in biological phenotype space |
title | The geometry of the Pareto front in biological phenotype space |
title_full | The geometry of the Pareto front in biological phenotype space |
title_fullStr | The geometry of the Pareto front in biological phenotype space |
title_full_unstemmed | The geometry of the Pareto front in biological phenotype space |
title_short | The geometry of the Pareto front in biological phenotype space |
title_sort | geometry of the pareto front in biological phenotype space |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3686184/ https://www.ncbi.nlm.nih.gov/pubmed/23789060 http://dx.doi.org/10.1002/ece3.528 |
work_keys_str_mv | AT sheftelhila thegeometryoftheparetofrontinbiologicalphenotypespace AT shovaloren thegeometryoftheparetofrontinbiologicalphenotypespace AT mayoavi thegeometryoftheparetofrontinbiologicalphenotypespace AT alonuri thegeometryoftheparetofrontinbiologicalphenotypespace AT sheftelhila geometryoftheparetofrontinbiologicalphenotypespace AT shovaloren geometryoftheparetofrontinbiologicalphenotypespace AT mayoavi geometryoftheparetofrontinbiologicalphenotypespace AT alonuri geometryoftheparetofrontinbiologicalphenotypespace |