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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Population genetics

Background:

  • Evolving systems constantly adapt to changing environments, seeking solutions in dynamic fitness landscapes.
  • Generalists maintain fitness across varied selective pressures, analogous to broadly neutralizing antibodies or multi-drug resistant microbes.
  • Understanding conditions favoring the evolution of generalists in complex, time-varying landscapes remains a challenge.

Purpose of the Study:

  • To investigate how environmental cycling influences the evolution of generalists in rugged fitness landscapes.
  • To determine the role of epistasis in non-randomly varying environments.
  • To develop a model for predicting the emergence of adaptable genotypes.

Main Methods:

  • Development of a generative model to assess the propensity of evolving generalists.
  • Analysis of rugged fitness landscapes with tunable relatedness and slow environmental alternation.
  • Estimation of environmental correlations from experimental fitness landscape topography.

Main Results:

  • Environmental cycling significantly enhances the search for fit generalists by expanding their effective basins of attraction.
  • Optimal emergence of high-performing generalists occurs at intermediate levels of landscape ruggedness and environmental relatedness.
  • The study provides a method to estimate environmental correlations from fitness landscape data.

Conclusions:

  • Environmental cycling is a key factor facilitating the evolution of adaptable generalists.
  • Intermediate ruggedness and environmental relatedness are optimal conditions for generalist emergence.
  • This framework offers strategies for developing broadly neutralizing antibodies and combating antibiotic resistance.