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  Engineering complex communities by directed evolution.

Chang, C.-Y., Vila, J. C. C., Bender, M., Li, R., Mankowski, M. C., Bassette, M., et al. (2021). Engineering complex communities by directed evolution. Nature ecology & evolution, 5(7), 1011-1023. doi:10.1038/s41559-021-01457-5.

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 Creators:
Chang, Chang-Yu, Author
Vila, Jean C C, Author
Bender, Madeline, Author
Li, Richard, Author
Mankowski, Madeleine C, Author
Bassette, Molly, Author
Borden, Julia, Author
Golfier, Stefan1, Author           
Sanchez, Paul Gerald L, Author
Waymack, Rachel, Author
Zhu, Xinwen, Author
Diaz-Colunga, Juan, Author
Estrela, Sylvie, Author
Rebolleda-Gomez, Maria, Author
Sanchez, Alvaro, Author
Affiliations:
1Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society, ou_2340692              

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 Abstract: Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.

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 Dates: 2021-07-01
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1038/s41559-021-01457-5
Other: cbg-8079
PMID: 33986540
 Degree: -

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Title: Nature ecology & evolution
  Other : Nat Ecol Evol
Source Genre: Journal
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Pages: - Volume / Issue: 5 (7) Sequence Number: - Start / End Page: 1011 - 1023 Identifier: -