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Cited by 263

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Massively parallel screening of synthetic microbial communities

Open access
Kehe, Jared, Kulesa, Anthony, Ortiz, Anthony, Ackerman, Cheri M., Thakku, Sri Gowtham, Sellers, Daniel, Kuehn, Seppe, Gore, Jeff, Friedman, Jonathan, Blainey, Paul C.·2019·Proceedings of the National Academy of Sciences

Abstract

Significance: Microbial communities have many applications, but current experimental strategies to investigate their behavior are limited by the combinatorial complexity of interactions between species. Here, we introduce a platform to automatically construct and test synthetic communities of microbes from a set of input species at a scale of ∼100,000 communities per day. As a first demonstration, we discovered specific compositions of bacteria isolated from local soil that promote the growth of a model plant symbiont. More broadly, our platform can be adopted for the discovery of microbial consortia with many useful properties, such as suppression of pathogens or degradation of recalcitrant substrates for use in biofuel production or environmental remediation.

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Crossref · 10.1073/pnas.1900102116

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Metadata

Authors
Kehe, J., Kulesa, A., Ortiz, A., Ackerman, C. M., Thakku, S. G., Sellers, D., Kuehn, S., Gore, J., Friedman, J., & Blainey, P. C.
Venue
Proceedings of the National Academy of Sciences
Published
2019-06-11
Volume
116
Issue
26
Pages
12804-12809
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2953098897
Cited by
263
References
6
FWCI
18.66
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Massively parallel screening of synthetic microbial communities. https://app.cochat.ai/papers/10.1073/pnas.1900102116
The paper
Kehe, J., Kulesa, A., Ortiz, A., Ackerman, C. M., Thakku, S. G., Sellers, D., Kuehn, S., Gore, J., Friedman, J., & Blainey, P. C. (2019). Massively parallel screening of synthetic microbial communities. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1900102116

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.