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Chirieleison SM, Allen PB, Simpson ZB, et al. Pattern transformation with DNA circuits. Nat Chem. 2013;5(12):1000-5doi: 10.1038/nchem.1764.
Chirieleison, S. M., Allen, P. B., Simpson, Z. B., Ellington, A. D., & Chen, X. (2013). Pattern transformation with DNA circuits. Nature chemistry, 5(12), 1000-5. https://doi.org/10.1038/nchem.1764
Chirieleison, Steven M, et al. "Pattern transformation with DNA circuits." Nature chemistry vol. 5,12 (2013): 1000-5. doi: https://doi.org/10.1038/nchem.1764
Chirieleison SM, Allen PB, Simpson ZB, Ellington AD, Chen X. Pattern transformation with DNA circuits. Nat Chem. 2013 Dec;5(12):1000-5. doi: 10.1038/nchem.1764. Epub 2013 Sep 29. PMID: 24256862; PMCID: PMC3970425.
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Nat Chem. 2013 Dec;5(12):1000-5. doi: 10.1038/nchem.1764. Epub 2013 Sep 29.

Pattern transformation with DNA circuits.

Nature chemistry

Steven M Chirieleison, Peter B Allen, Zack B Simpson, Andrew D Ellington, Xi Chen

Affiliations

  1. Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, USA.

PMID: 24256862 PMCID: PMC3970425 DOI: 10.1038/nchem.1764

Abstract

Readily programmable chemical networks are important tools as the scope of chemistry expands from individual molecules to larger molecular systems. Although many complex systems are constructed using conventional organic and inorganic chemistry, the programmability of biological molecules such as nucleic acids allows for precise, high-throughput and automated design, as well as simple, rapid and robust implementation. Here we show that systematic and quantitative control over the diffusivity and reactivity of DNA molecules yields highly programmable chemical reaction networks (CRNs) that execute at the macroscale. In particular, we designed and implemented non-enzymatic DNA circuits capable of performing pattern-transformation algorithms such as edge detection. We also showed that it is possible to fine-tune and multiplex such circuits. We believe these strategies will provide programmable platforms on which to prototype CRNs, discover bottom-up construction principles and generate patterns in materials.

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