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Cited by 931

The DOI matches its Crossref record; 209 of the 209 references it cites were found in a registry or an index.

Principles of genetic circuit design

Open access
Brophy, Jennifer A N, Voigt, Christopher A·2014·Nature Methods

Abstract

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.

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Crossref · 10.1038/nmeth.2926

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Metadata

Authors
Brophy, J. A. N., & Voigt, C. A.
Venue
Nature Methods
Published
2014-04-29
Volume
11
Issue
5
Pages
508-520
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W2118464978
Cited by
931
References
209
FWCI
26.40
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Principles of genetic circuit design. https://app.cochat.ai/papers/10.1038/nmeth.2926
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Brophy, J. A. N., & Voigt, C. A. (2014). Principles of genetic circuit design. Nature Methods. https://doi.org/10.1038/nmeth.2926

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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