The geometry of the Pareto front in biological phenotype space
| Autor: | Avi Mayo, Oren Shoval, Hila Sheftel, Uri Alon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
0106 biological sciences
Computer science Monotonic function Space (mathematics) 010603 evolutionary biology 01 natural sciences Multi-objective optimization Task (project management) 03 medical and health sciences Line segment Simple (abstract algebra) evolutionary theory efficiency front Ecology Evolution Behavior and Systematics Selection (genetic algorithm) 030304 developmental biology Nature and Landscape Conservation Original Research 0303 health sciences Ecology business.industry multi-objective optimality evolutionary trade-offs location theory Range (mathematics) Ecological morphology Artificial intelligence business Algorithm |
| Zdroj: | Ecology and Evolution |
| ISSN: | 2045-7758 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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