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Modelling and simulating Lenski's long-term evolution experiment.

Ellen Baake1, Adrián González Casanova2, Sebastian Probst1

  • 1University of Bielefeld, Faculty of Technology, 33615 Bielefeld, Germany.

Theoretical Population Biology
|April 12, 2019
PubMed
Summary
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This study refines a model of Lenski

Area of Science:

  • Evolutionary Biology
  • Population Genetics
  • Mathematical Modeling

Background:

  • Lenski's long-term evolution experiment provides a unique dataset for studying microbial evolution.
  • Previous models, like Wiser et al. (2013), describe mean fitness increase due to beneficial mutations.
  • Understanding the dynamics of adaptation and the role of epistasis is crucial in evolutionary studies.

Purpose of the Study:

  • To conceptually and mathematically enhance the Wiser et al. (2013) model.
  • To analyze daily growth dynamics and the impact of epistasis on mean fitness.
  • To investigate the runtime effect and its relation to increasing fitness.

Main Methods:

  • Utilized a Cannings model incorporating mutation and selection with diminishing returns epistasis.
Keywords:
Cannings modelClonal interferenceEpistasisLenski’s long-term evolution experimentOffspring varianceRuntime effect

Related Experiment Videos

  • Developed mathematical frameworks, including a law of large numbers for infinite populations.
  • Employed heuristic approximations and simulations for finite populations.

    • Main Results:

    • Identified a 'runtime effect' where daily growth periods shorten as fitness increases.
    • Clarified the contribution of epistasis to the overall mean fitness curve.
    • Provided rigorous mathematical results for asymptotic parameter regimes and finite populations.

    Conclusions:

    • The enhanced model offers deeper insights into the daily dynamics of adaptation.
    • Epistasis plays a significant role in shaping the mean fitness trajectory.
    • The study bridges theoretical models with empirical observations from long-term evolution experiments.