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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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/1/35 |
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| author | Oleg Kuzenkov Andrew Morozov Galina Kuzenkova |
| author_facet | Oleg Kuzenkov Andrew Morozov Galina Kuzenkova |
| author_sort | Oleg Kuzenkov |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T13:42:15Z |
| format | Article |
| id | doaj.art-08c09e841e394e92ae1ce931081f3c64 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:42:15Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-08c09e841e394e92ae1ce931081f3c642023-11-21T02:55:42ZengMDPI AGEntropy1099-43002020-12-012313510.3390/e23010035Exploring Evolutionary Fitness in Biological Systems Using Machine Learning MethodsOleg Kuzenkov0Andrew Morozov1Galina Kuzenkova2Department of Differential Equations, Mathematical and Numerical Analysis, Lobachevsky State University, 603950 Nizhni Novgorod, RussiaSchool of Mathematics and Actuarial Science, University of Leicester, Leicester LE1 7RH, UKDepartment of Differential Equations, Mathematical and Numerical Analysis, Lobachevsky State University, 603950 Nizhni Novgorod, RussiaHere, 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.https://www.mdpi.com/1099-4300/23/1/35zooplanktondiel vertical migrationevolutionarily stable strategyevolutionary fitnessranking ordermachine-learned ranking |
| spellingShingle | Oleg Kuzenkov Andrew Morozov Galina Kuzenkova Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods Entropy zooplankton diel vertical migration evolutionarily stable strategy evolutionary fitness ranking order machine-learned ranking |
| 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 | zooplankton diel vertical migration evolutionarily stable strategy evolutionary fitness ranking order machine-learned ranking |
| url | https://www.mdpi.com/1099-4300/23/1/35 |
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