ArticleiScience2023
Partner-assisted artificial selection of a secondary function for efficient bioremediation.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 7 citations in OpenAlex.
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial enzymes can address diverse challenges such as degradation of toxins. However, if the function of interest does not confer a sufficient fitness effect on the producer, the enzymatic function cannot be improved in the host cells by a conventional selection scheme. To overcome this limitation, we propose an alternative scheme, termed "partner-assisted artificial selection" (PAAS), wherein the population of enzyme producers is assisted by function-dependent feedback from an accessory population. Simulations investigating the efficiency of toxin degradation reveal that this strategy supports selection of improved degradation performance, which is robust to stochasticity in the model parameters. We observe that conventional considerations still apply in PAAS: more restrictive bottlenecks lead to stronger selection but add uncertainty. Overall, we offer a guideline for successful implementation of PAAS and highlight its potentials and limitations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.