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Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods

Here, we propose a computational approach to explore evolutionary fitness in complex biological systems based on empirical data using artificial neural networks. The essence of our approach is the following. We first introduce a ranking order of inherited elements (behavioral strategies or/and life...

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Detalles Bibliográficos
Autores principales: Kuzenkov, Oleg, Morozov, Andrew, Kuzenkova, Galina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824698/
https://www.ncbi.nlm.nih.gov/pubmed/33383722
http://dx.doi.org/10.3390/e23010035
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author Kuzenkov, Oleg
Morozov, Andrew
Kuzenkova, Galina
author_facet Kuzenkov, Oleg
Morozov, Andrew
Kuzenkova, Galina
author_sort Kuzenkov, Oleg
collection PubMed
description Here, we propose a computational approach to explore evolutionary fitness in complex biological systems based on empirical data using artificial neural networks. The essence of our approach is the following. We first introduce a ranking order of inherited elements (behavioral strategies or/and life history traits) in considered self-reproducing systems: we use available empirical information on selective advantages of such elements. Next, we introduce evolutionary fitness, which is formally described as a certain function reflecting the introduced ranking order. Then, we approximate fitness in the space of key parameters using a Taylor expansion. To estimate the coefficients in the Taylor expansion, we utilize artificial neural networks: we construct a surface to separate the domains of superior and interior ranking of pair inherited elements in the space of parameters. Finally, we use the obtained approximation of the fitness surface to find the evolutionarily stable (optimal) strategy which maximizes fitness. As an ecologically important study case, we apply our approach to explore the evolutionarily stable diel vertical migration of zooplankton in marine and freshwater ecosystems. Using machine learning we reconstruct the fitness function of herbivorous zooplankton from empirical data and predict the daily trajectory of a dominant species in the northeastern Black Sea.
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spelling pubmed-78246982021-02-24 Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods Kuzenkov, Oleg Morozov, Andrew Kuzenkova, Galina Entropy (Basel) Article Here, we propose a computational approach to explore evolutionary fitness in complex biological systems based on empirical data using artificial neural networks. The essence of our approach is the following. We first introduce a ranking order of inherited elements (behavioral strategies or/and life history traits) in considered self-reproducing systems: we use available empirical information on selective advantages of such elements. Next, we introduce evolutionary fitness, which is formally described as a certain function reflecting the introduced ranking order. Then, we approximate fitness in the space of key parameters using a Taylor expansion. To estimate the coefficients in the Taylor expansion, we utilize artificial neural networks: we construct a surface to separate the domains of superior and interior ranking of pair inherited elements in the space of parameters. Finally, we use the obtained approximation of the fitness surface to find the evolutionarily stable (optimal) strategy which maximizes fitness. As an ecologically important study case, we apply our approach to explore the evolutionarily stable diel vertical migration of zooplankton in marine and freshwater ecosystems. Using machine learning we reconstruct the fitness function of herbivorous zooplankton from empirical data and predict the daily trajectory of a dominant species in the northeastern Black Sea. MDPI 2020-12-29 /pmc/articles/PMC7824698/ /pubmed/33383722 http://dx.doi.org/10.3390/e23010035 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kuzenkov, Oleg
Morozov, Andrew
Kuzenkova, Galina
Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods
title Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods
title_full Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods
title_fullStr Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods
title_full_unstemmed Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods
title_short Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods
title_sort exploring evolutionary fitness in biological systems using machine learning methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824698/
https://www.ncbi.nlm.nih.gov/pubmed/33383722
http://dx.doi.org/10.3390/e23010035
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