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Genome complexity, robustness and genetic interactions in digital organisms

Author

Listed:
  • Richard E. Lenski

    (Center for Microbial Ecology, Michigan State University)

  • Charles Ofria

    (Computation and Neural Systems, California Institute of Technology)

  • Travis C. Collier

    (Ecology, and Evolution, University of California)

  • Christoph Adami

    (Kellogg Radiation Laboratory, California Institute of Technology)

Abstract

Digital organisms are computer programs that self-replicate, mutate and adapt by natural selection1,2,3. They offer an opportunity to test generalizations about living systems that may extend beyond the organic life that biologists usually study. Here we have generated two classes of digital organism: simple programs selected solely for rapid replication, and complex programs selected to perform mathematical operations that accelerate replication through a set of defined ‘metabolic’ rewards. To examine the differences in their genetic architecture, we introduced millions of single and multiple mutations into each organism and measured the effects on the organism's fitness. The complex organisms are more robust than the simple ones with respect to the average effects of single mutations. Interactions among mutations are common and usually yield higher fitness than predicted from the component mutations assuming multiplicative effects; such interactions are especially important in the complex organisms. Frequent interactions among mutations have also been seen in bacteria, fungi and fruitflies4,5,6. Our findings support the view that interactions are a general feature of genetic systems7,8,9.

Suggested Citation

  • Richard E. Lenski & Charles Ofria & Travis C. Collier & Christoph Adami, 1999. "Genome complexity, robustness and genetic interactions in digital organisms," Nature, Nature, vol. 400(6745), pages 661-664, August.
    • Handle: RePEc:nat:nature:v:400:y:1999:i:6745:d:10.1038_23245
    DOI: 10.1038/23245
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    Cited by:

    1. Anna Y. Alekseeva & Anneloes E. Groenenboom & Eddy J. Smid & Sijmen E. Schoustra, 2021. "Eco-Evolutionary Dynamics in Microbial Communities from Spontaneous Fermented Foods," IJERPH, MDPI, vol. 18(19), pages 1-19, September.
    2. Peacor, Scott D. & Allesina, Stefano & Riolo, Rick L. & Hunter, Tim S., 2007. "A new computational system, DOVE (Digital Organisms in a Virtual Ecosystem), to study phenotypic plasticity and its effects in food webs," Ecological Modelling, Elsevier, vol. 205(1), pages 13-28.
    3. Clark, James R. & Daines, Stuart J. & Lenton, Timothy M. & Watson, Andrew J. & Williams, Hywel T.P., 2011. "Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters," Ecological Modelling, Elsevier, vol. 222(23), pages 3823-3837.
    4. Liberman, Uri & Feldman, Marcus, 2008. "On the evolution of epistasis III: The haploid case with mutation," Theoretical Population Biology, Elsevier, vol. 73(2), pages 307-316.
    5. 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.
    6. MacPherson, Brian & Gras, Robin, 2016. "Individual-based ecological models: Adjunctive tools or experimental systems?," Ecological Modelling, Elsevier, vol. 323(C), pages 106-114.

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