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Simulations reveal challenges to artificial community selection and possible strategies for success

Li Xie, Alex E. Yuan, Wenying Shou
doi: https://doi.org/10.1101/264689
Li Xie
Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109
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Alex E. Yuan
Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109
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Wenying Shou
Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109
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Abstract

Multi-species microbial communities often display “community functions” arising from interactions of member species. Interactions are often difficult to decipher, making it challenging to design communities with desired functions. Alternatively, similar to artificial selection for individuals in agriculture and industry, one could repeatedly choose communities with the highest community functions to reproduce by randomly partitioning each into multiple “Newborn” communities for the next cycle. However, previous efforts in selecting complex communities have generated mixed outcomes that are difficult to interpret. To understand how to effectively enact community selection, we simulated community selection to improve a community function that requires two species and imposes a fitness cost on one or both species. Our simulations predict that improvement could be easily stalled unless various aspects of selection, including promoting species coexistence, suppressing non-contributors, adopting a “bet-hedging” strategy when choosing communities to reproduce, and reducing stochastic fluctuations in species biomass of Newborn communities, were carefully considered. When these considerations were addressed in experimentally feasible manners, community selection could overcome natural selection to improve community function, and may even force species to evolve growth restraint to achieve species coexistence. Our conclusions hold under various alternative model assumptions, and are thus applicable to a variety of communities.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 20, 2019.
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Simulations reveal challenges to artificial community selection and possible strategies for success
Li Xie, Alex E. Yuan, Wenying Shou
bioRxiv 264689; doi: https://doi.org/10.1101/264689
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Simulations reveal challenges to artificial community selection and possible strategies for success
Li Xie, Alex E. Yuan, Wenying Shou
bioRxiv 264689; doi: https://doi.org/10.1101/264689

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