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The evolutionary origin of complex features

Author

Listed:
  • Richard E. Lenski

    (Michigan State University)

  • Charles Ofria

    (Michigan State University)

  • Robert T. Pennock

    (Michigan State University)

  • Christoph Adami

    (California Institute of Technology)

Abstract

A long-standing challenge to evolutionary theory has been whether it can explain the origin of complex organismal features. We examined this issue using digital organisms—computer programs that self-replicate, mutate, compete and evolve. Populations of digital organisms often evolved the ability to perform complex logic functions requiring the coordinated execution of many genomic instructions. Complex functions evolved by building on simpler functions that had evolved earlier, provided that these were also selectively favoured. However, no particular intermediate stage was essential for evolving complex functions. The first genotypes able to perform complex functions differed from their non-performing parents by only one or two mutations, but differed from the ancestor by many mutations that were also crucial to the new functions. In some cases, mutations that were deleterious when they appeared served as stepping-stones in the evolution of complex features. These findings show how complex functions can originate by random mutation and natural selection.

Suggested Citation

  • Richard E. Lenski & Charles Ofria & Robert T. Pennock & Christoph Adami, 2003. "The evolutionary origin of complex features," Nature, Nature, vol. 423(6936), pages 139-144, May.
    • Handle: RePEc:nat:nature:v:423:y:2003:i:6936:d:10.1038_nature01568
    DOI: 10.1038/nature01568
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    Cited by:

    1. Mirzadeh Phirouzabadi, Amir & Savage, David & Blackmore, Karen & Juniper, James, 2020. "The evolution of dynamic interactions between the knowledge development of powertrain systems," Transport Policy, Elsevier, vol. 93(C), pages 1-16.
    2. Deck, Cary & Sarangi, Sudipta & Wiser, Matt, 2017. "An experimental investigation of simultaneous multi-battle contests with strategic complementarities," Journal of Economic Psychology, Elsevier, vol. 63(C), pages 117-134.
    3. Dimitris Iliopoulos & Arend Hintze & Christoph Adami, 2010. "Critical Dynamics in the Evolution of Stochastic Strategies for the Iterated Prisoner's Dilemma," PLOS Computational Biology, Public Library of Science, vol. 6(10), pages 1-8, October.
    4. Jeffrey A Edlund & Nicolas Chaumont & Arend Hintze & Christof Koch & Giulio Tononi & Christoph Adami, 2011. "Integrated Information Increases with Fitness in the Evolution of Animats," PLOS Computational Biology, Public Library of Science, vol. 7(10), pages 1-13, October.
    5. Steve O'Hagan & Joshua Knowles & Douglas B Kell, 2012. "Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-14, November.
    6. Bogna J. Smug & Krzysztof Szczepaniak & Eduardo P. C. Rocha & Stanislaw Dunin-Horkawicz & Rafał J. Mostowy, 2023. "Ongoing shuffling of protein fragments diversifies core viral functions linked to interactions with bacterial hosts," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    7. Briscoe, Gerard & De Wilde, Philippe, 2011. "Physical complexity of variable length symbolic sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3732-3741.
    8. Andrea Maesani & Pradeep Ruben Fernando & Dario Floreano, 2014. "Artificial Evolution by Viability Rather than Competition," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
    9. Hellweger, Ferdi L. & Bucci, Vanni, 2009. "A bunch of tiny individuals—Individual-based modeling for microbes," Ecological Modelling, Elsevier, vol. 220(1), pages 8-22.
    10. Tracy Chih-Ting Koubkova-Yu & Jung-Chi Chao & Jun-Yi Leu, 2018. "Heterologous Hsp90 promotes phenotypic diversity through network evolution," PLOS Biology, Public Library of Science, vol. 16(11), pages 1-29, November.
    11. Miguel A Fortuna & Luis Zaman & Charles Ofria & Andreas Wagner, 2017. "The genotype-phenotype map of an evolving digital organism," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-20, February.

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