Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

  1. Alexander Lalejini  Is a corresponding author
  2. Emily Dolson
  3. Anya E Vostinar
  4. Luis Zaman  Is a corresponding author
  1. University of Michigan-Ann Arbor, United States
  2. Michigan State University, United States
  3. Carleton College, United States

Abstract

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask if parent selection algorithms-procedures for choosing promising progenitors-from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top-10% selection). We found that multi-objective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multi-objective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.

Data availability

Our source code for experiments, analyses, and visualizations is publicly available on GitHub (https://github.com/amlalejini/directed-digital-evolution). Our GitHub repository is publicly archived using Zenodo with the following DOI: 10.5281/zenodo.6403135.The data produced by our computational experiments are publicly available and archived on the Open Science Framework: https://osf.io/zn63x/ (DOI: 10.17605/OSF.IO/ZN63X).

The following data sets were generated

Article and author information

Author details

  1. Alexander Lalejini

    University of Michigan-Ann Arbor, Ann Arbor, United States
    For correspondence
    lalejini@umich.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0994-2718
  2. Emily Dolson

    Department of Computer Science and Engineering, Michigan State University, East Lansing, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Anya E Vostinar

    Computer Science Department, Carleton College, Northfield, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7216-5283
  4. Luis Zaman

    University of Michigan-Ann Arbor, Ann Arbor, United States
    For correspondence
    zamanlh@umich.edu
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Science Foundation (DEB-1813069)

  • Luis Zaman

National Science Foundation (MCB-1750125)

  • Anya E Vostinar

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. C Brandon Ogbunugafor, Yale University, United States

Publication history

  1. Received: April 21, 2022
  2. Accepted: August 1, 2022
  3. Accepted Manuscript published: August 2, 2022 (version 1)

Copyright

© 2022, Lalejini et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Alexander Lalejini
  2. Emily Dolson
  3. Anya E Vostinar
  4. Luis Zaman
(2022)
Artificial selection methods from evolutionary computing show promise for directed evolution of microbes
eLife 11:e79665.
https://doi.org/10.7554/eLife.79665

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