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Artificial selection methods from evolutionary computing show promise for directed evolution of microbes
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444240/ https://www.ncbi.nlm.nih.gov/pubmed/35916365 http://dx.doi.org/10.7554/eLife.79665 |
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author | Lalejini, Alexander Dolson, Emily Vostinar, Anya E Zaman, Luis |
author_facet | Lalejini, Alexander Dolson, Emily Vostinar, Anya E Zaman, Luis |
author_sort | Lalejini, Alexander |
collection | PubMed |
description | 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 whether 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 multiobjective 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 multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods. |
format | Online Article Text |
id | pubmed-9444240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-94442402022-09-06 Artificial selection methods from evolutionary computing show promise for directed evolution of microbes Lalejini, Alexander Dolson, Emily Vostinar, Anya E Zaman, Luis eLife Computational and Systems Biology 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 whether 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 multiobjective 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 multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods. eLife Sciences Publications, Ltd 2022-08-02 /pmc/articles/PMC9444240/ /pubmed/35916365 http://dx.doi.org/10.7554/eLife.79665 Text en © 2022, Lalejini et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Lalejini, Alexander Dolson, Emily Vostinar, Anya E Zaman, Luis Artificial selection methods from evolutionary computing show promise for directed evolution of microbes |
title | Artificial selection methods from evolutionary computing show promise for directed evolution of microbes |
title_full | Artificial selection methods from evolutionary computing show promise for directed evolution of microbes |
title_fullStr | Artificial selection methods from evolutionary computing show promise for directed evolution of microbes |
title_full_unstemmed | Artificial selection methods from evolutionary computing show promise for directed evolution of microbes |
title_short | Artificial selection methods from evolutionary computing show promise for directed evolution of microbes |
title_sort | artificial selection methods from evolutionary computing show promise for directed evolution of microbes |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444240/ https://www.ncbi.nlm.nih.gov/pubmed/35916365 http://dx.doi.org/10.7554/eLife.79665 |
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