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Can evolution paths be explained by chance alone?

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  • 1Department of Mathematics, University of Colorado at Colorado Springs, United States.

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|February 10, 2019
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Summary
This summary is machine-generated.

A new probabilistic model predicts that population fitness increases in ln(n) steps after n births. This general law for evolutionary jumps, observed in simulations, matches experimental findings in bacteria evolution.

Keywords:
Adaptive walkEvolutionProbability model

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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Theoretical biology

Background:

  • Understanding the dynamics of population maximum fitness evolution is crucial in evolutionary biology.
  • Previous studies have observed specific patterns in evolutionary trajectories, such as those in Lenski and Travisano's long-term bacteria experiments.

Purpose of the Study:

  • To propose a novel, purely probabilistic model for the evolution of population maximum fitness.
  • To identify generalizable laws governing the number and pattern of fitness "upwards jumps" during evolution.

Main Methods:

  • Development of a theoretical probabilistic model for population evolution.
  • Mathematical derivation of the relationship between population size (n births) and the number of fitness jumps.
  • Computer simulations to visualize and analyze typical evolution paths and compare with experimental data.

Main Results:

  • The model predicts approximately ln(n) upwards jumps in population fitness after n births.
  • This relationship holds irrespective of mutation probability and fitness distribution, suggesting a universal law.
  • Simulations reveal evolution paths characterized by initial rapid increases followed by extended periods of stasis (plateaux).
  • Independent simulation runs exhibit parallel evolutionary paths, mirroring experimental observations.

Conclusions:

  • The proposed probabilistic model offers a generalizable explanation for the number of fitness jumps during evolution.
  • The model's predictions align with empirical observations from long-term experimental evolution studies, validating its applicability.
  • The findings suggest that evolutionary trajectories, despite underlying stochasticity, may follow predictable patterns of progress and stasis.