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Evolution of digital organisms at high mutation rates leads to survival of the flattest

Author

Listed:
  • Claus O. Wilke

    (Digital Life Laboratory, Mail Code 136-93, California Institute of Technology)

  • Jia Lan Wang

    (Digital Life Laboratory, Mail Code 136-93, California Institute of Technology)

  • Charles Ofria

    (Center for Biological Modeling, Michigan State University)

  • Richard E. Lenski

    (Center for Biological Modeling, Michigan State University)

  • Christoph Adami

    (Digital Life Laboratory, Mail Code 136-93, California Institute of Technology
    Jet Propulsion Laboratory, Mail Code 126-347, California Institute of Technology)

Abstract

Darwinian evolution favours genotypes with high replication rates, a process called ‘survival of the fittest’. However, knowing the replication rate of each individual genotype may not suffice to predict the eventual survivor, even in an asexual population. According to quasi-species theory, selection favours the cloud of genotypes, interconnected by mutation, whose average replication rate is highest1,2,3,4,5. Here we confirm this prediction using digital organisms that self-replicate, mutate and evolve6,7,8,9. Forty pairs of populations were derived from 40 different ancestors in identical selective environments, except that one of each pair experienced a 4-fold higher mutation rate. In 12 cases, the dominant genotype that evolved at the lower mutation rate achieved a replication rate >1.5-fold faster than its counterpart. We allowed each of these disparate pairs to compete across a range of mutation rates. In each case, as mutation rate was increased, the outcome of competition switched to favour the genotype with the lower replication rate. These genotypes, although they occupied lower fitness peaks, were located in flatter regions of the fitness surface and were therefore more robust with respect to mutations.

Suggested Citation

  • Claus O. Wilke & Jia Lan Wang & Charles Ofria & Richard E. Lenski & Christoph Adami, 2001. "Evolution of digital organisms at high mutation rates leads to survival of the flattest," Nature, Nature, vol. 412(6844), pages 331-333, July.
  • Handle: RePEc:nat:nature:v:412:y:2001:i:6844:d:10.1038_35085569
    DOI: 10.1038/35085569
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    Cited by:

    1. Arturo Marín & Héctor Tejero & Juan Carlos Nuño & Francisco Montero, 2013. "The Advantage of Arriving First: Characteristic Times in Finite Size Populations of Error-Prone Replicators," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    2. Lukas Aufinger & Johann Brenner & Friedrich C. Simmel, 2022. "Complex dynamics in a synchronized cell-free genetic clock," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Proulx, Stephen R., 2011. "The rate of multi-step evolution in Moran and Wright–Fisher populations," Theoretical Population Biology, Elsevier, vol. 80(3), pages 197-207.
    4. Elizabeth Aston & Alastair Channon & Charles Day & Christopher G Knight, 2013. "Critical Mutation Rate Has an Exponential Dependence on Population Size in Haploid and Diploid Populations," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.
    5. Ravi Kashyap, 2019. "Concepts, Components and Collections of Trading Strategies and Market Color," Papers 1910.02144, arXiv.org, revised Jan 2020.
    6. Tobias Sikosek & Erich Bornberg-Bauer & Hue Sun Chan, 2012. "Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts," PLOS Computational Biology, Public Library of Science, vol. 8(9), pages 1-17, September.

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