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Exploring Evolutionary Fitness in Biological Systems Using Machine Learning Methods.

Oleg Kuzenkov1, Andrew Morozov2,3, Galina Kuzenkova1

  • 1Department of Differential Equations, Mathematical and Numerical Analysis, Lobachevsky State University, 603950 Nizhni Novgorod, Russia.

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Summary

This study introduces a computational method using artificial neural networks to determine evolutionary fitness from empirical data. It successfully models zooplankton migration, predicting species trajectories in the Black Sea.

Keywords:
diel vertical migrationevolutionarily stable strategyevolutionary fitnessmachine-learned rankingpattern recognitionranking orderzooplankton

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Machine Learning Applications

Background:

  • Understanding evolutionary fitness in complex biological systems is crucial for predicting adaptation and stability.
  • Existing methods often struggle to integrate diverse empirical data for fitness estimation.
  • Artificial neural networks offer a powerful tool for modeling complex, non-linear biological functions.

Purpose of the Study:

  • To develop a novel computational approach for exploring evolutionary fitness using artificial neural networks and empirical data.
  • To approximate the fitness function in parameter space via Taylor expansion and neural network-based coefficient estimation.
  • To identify evolutionarily stable strategies by maximizing the approximated fitness surface.

Main Methods:

  • Introduced a ranking order for inherited elements based on selective advantages from empirical data.
  • Defined evolutionary fitness as a function reflecting this ranking order.
  • Employed artificial neural networks to estimate Taylor expansion coefficients by constructing a separating surface for element rankings in parameter space.

Main Results:

  • Successfully applied the computational approach to study the evolutionarily stable diel vertical migration of zooplankton.
  • Reconstructed the fitness function of herbivorous zooplankton using machine learning and empirical data.
  • Predicted the daily migration trajectory of a dominant zooplankton species in the northeastern Black Sea.

Conclusions:

  • The proposed computational framework effectively models evolutionary fitness and identifies optimal strategies in complex biological systems.
  • This data-driven approach, utilizing artificial neural networks, provides a robust method for ecological and evolutionary predictions.
  • The study demonstrates the utility of machine learning in reconstructing fitness landscapes and predicting species behavior, exemplified by zooplankton migration.