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Cited by 284

The DOI matches its Crossref record; 160 of the 160 references it cites were found in a registry or an index.

Building machines with DNA molecules

Open access
Ramezani, Hamid, Dietz, Hendrik·2020·Nature Reviews Genetics

Abstract

In nature, DNA molecules carry the hereditary information. But DNA has physical and chemical properties that make it attractive for uses beyond heredity. In this Review, we discuss the potential of DNA for creating machines that are both encoded by and built from DNA molecules. We review the main methods of DNA nanostructure assembly, describe recent advances in building increasingly complex molecular structures and discuss strategies for creating machine-like nanostructures that can be actuated and move. We highlight opportunities for applications of custom DNA nanostructures as scientific tools to address challenges across biology, chemistry and engineering. This Review discusses the potential of DNA for creating machines that are both encoded by and built from DNA molecules. Alongside an overview of DNA nanostructure assembly, the authors describe recent advances and the remaining challenges, highlighting applications of custom DNA nanostructures as scientific tools.

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Crossref · 10.1038/s41576-019-0175-6

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Metadata

Authors
Ramezani, H., & Dietz, H.
Venue
Nature Reviews Genetics
Published
2019-10-21
Volume
21
Issue
1
Pages
5-26
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
OpenAlex
W2980497132
Cited by
284
References
160
FWCI
2.55
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Building machines with DNA molecules. https://app.cochat.ai/papers/10.1038/s41576-019-0175-6
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Ramezani, H., & Dietz, H. (2020). Building machines with DNA molecules. Nature Reviews Genetics. https://doi.org/10.1038/s41576-019-0175-6

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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.