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Diverse and robust molecular algorithms using reprogrammable DNA self-assembly
Abstract
Molecular biology provides an inspiring proof-of-principle that chemical systems can store and process information to direct molecular activities such as the fabrication of complex structures from molecular components. To develop information-based chemistry as a technology for programming matter to function in ways not seen in biological systems, it is necessary to understand how molecular interactions can encode and execute algorithms. The self-assembly of relatively simple units into complex products 1 is particularly well suited for such investigations. Theory that combines mathematical tiling and statistical-mechanical models of molecular crystallization has shown that algorithmic behaviour can be embedded within molecular self-assembly processes 2,3, and this has been experimentally demonstrated using DNA nanotechnology 4 with up to 22 tile types 5-11. However, many information technologies exhibit a complexity threshold-such as the minimum transistor count needed for a general-purpose computer-beyond which the power of a reprogrammable system increases qualitatively, and it has been unclear whether the biophysics of DNA self-assembly allows that threshold to be exceeded. Here we report the design and experimental validation of a DNA tile set that contains 355 single-stranded tiles and can, through simple tile selection, be reprogrammed to implement a wide variety of 6-bit algorithms. We use this set to construct 21 circuits that execute algorithms including copying, sorting, recognizing palindromes and multiples of 3, random walking, obtaining an unbiased choice from a biased random source, electing a leader, simulating cellular automata, generating deterministic and randomized patterns, and counting to 63, with an overall per-tile error rate of less than 1 in 3,000. These findings suggest that molecular self-assembly could be a reliable algorithmic component within programmable chemical systems. The development of molecular machines that are reprogrammable-at a high level of abstraction and thus without requiring knowledge of the underlying physics-will establish a creative space in which molecular programmers can flourish.
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“Past applications of this idea include the design of a set of TX tiles that carry out a cumulative XOR operation, a set of DX tiles that self-assemble into a Sierpinski triangle, and impressively, a set of 355 SSTs that can be used to produce a variety of cellular automata capable of carrying out a number of computational tasks (Figure 11).144”
From Emerging Approaches to DNA Data Storage: Challenges and Prospects (2022) · cites this paper as [144]
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“Particularly interesting in the latter example is the ability to controllably reintroduce indeterminism by including a plurality of tiles that could fill a given niche and leaving the ultimately realized pattern up to competition.”
From Emerging Approaches to DNA Data Storage: Challenges and Prospects (2022) · cites this paper as [144]
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Citations
Cites
Whitesides, G. M., & Grzybowski, B. A. (2002) · Science · doi:10.1126/science.1070821 · Cited by 7,550
Folding DNA to create nanoscale shapes and patterns
Rothemund, P. W. K. (2006) · Nature · doi:10.1038/nature04586 · Cited by 7,474
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-26 · Cited by 5,098
Design and self-assembly of two-dimensional DNA crystals
Winfree, E., Liu, F., Wenzler, L. A., & Seeman, N. C. (1998) · Nature · doi:10.1038/28998 · Cited by 2,857
Self-citationSeeman, N. C., & Sleiman, H. F. (2017) · Nature Reviews Materials · doi:10.1038/natrevmats.2017.68 · Cited by 1,875
NUPACK: Analysis and design of nucleic acid systems
Zadeh, J. N., Steenberg, C. D., Bois, J. S., Wolfe, B. R., Pierce, M. B., Khan, A. R., Dirks, R. M., & Pierce, N. A. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21596 · Cited by 1,670
Complex shapes self-assembled from single-stranded DNA tiles
Wei, B., Dai, M., & Yin, P. (2012) · Nature · doi:10.1038/nature11075 · Cited by 1,012
Self-citationAlgorithmic Self-Assembly of DNA Sierpinski Triangles
Rothemund, P. W. K., Papadakis, N., & Winfree, E. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020424 · Cited by 860
Self-citationLogical computation using algorithmic self-assembly of DNA triple-crossover molecules
Mao, C., LaBean, T. H., Reif, J. H., & Seeman, N. C. (2000) · Nature · doi:10.1038/35035038 · Cited by 779
Universality in Elementary Cellular Automata
Cook, M. (2004) · Complex Systems · doi:10.25088/complexsystems.15.1.1 · Cited by 758
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Items 101–200 of 295
An, X., Meng, Z., Wang, Y., & Sun, J. (2022) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00509 · Cited by 15
Bai, D., Zhang, J., Zhang, Y., Yu, H., Zhang, L., Han, X., Lv, K., Wang, L., Wang, L., Wu, Y., et al. (2023) · Small · doi:10.1002/smll.202307421 · Cited by 15
Oritatami: A Computational Model for Molecular Co-Transcriptional Folding
Geary, C. W., Meunier, P.-É., Schabanel, N., & Seki, S. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20092259 · Cited by 15
Multiform DNA origami arrays using minimal logic control
Chen, C., Xu, J., & Shi, X. (2020) · Nanoscale · doi:10.1039/d0nr00783h · Cited by 15
Jing, X., Kroneberg, N., Peil, A., Renz, B., Ding, L. L., Heil, T., Hipp, K., van Aken, P. A., Yan, H., & Liu, N. (2025) · Nature Nanotechnology · doi:10.1038/s41565-025-01976-3 · Cited by 16
A molecular computing approach to solving optimization problems via programmable microdroplet arrays
Guo, S. Y., Friederich, P., Cao, Y., Wu, T., Forman, C. J., Mendoza, D., Degroote, M., Cavell, A. C., Krasecki, V. K., Hickman, R. J., et al. (2021) · Matter · doi:10.1016/j.matt.2021.03.002 · Cited by 15
Biotemplated precise assembly approach toward ultra-scaled high-performance electronics
Chen, Y., Zhao, M., Ouyang, Y., Zhang, S., Liu, Z., Wang, K., Zhang, Z., Liu, Y., Yang, C. J., Sun, W., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00870-3 · Cited by 14
DNA Logic Gates for Small Molecule Activation Circuits in Cells
Emanuelson, C., Bardhan, A., & Deiters, A. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00474 · Cited by 16
Self-assembled cell-scale containers made from DNA origami membranes
Karfusehr, C., Eder, M., Yang, H. Y., Beinsteiner, B., Jasnin, M., & Simmel, F. C. (2025) · Nature Materials · doi:10.1038/s41563-025-02418-0 · Cited by 15
Sensitive Oligodeoxynucleotide Synthesis Using Dim and Dmoc as Protecting Groups
Shahsavari, S., Eriyagama, D. N. A. M., Chen, J., Halami, B., Yin, Y., Chillar, K., & Fang, S. (2019) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.9b01527 · Cited by 14
DNA nanotechnology-empowered finite state machines
Cao, S., Wang, F., Wang, L., Fan, C., & Li, J. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00060a · Cited by 16
Design principles for construction of DNA-based nanostructures
Chen, C., Xia, X., Tian, C., Zhang, Z., Jin, J., Huang, C. Z., Zuo, H., & Mao, C. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115679 · Cited by 14
Prime factorization via localized tile assembly in a DNA origami framework
Zhang, Y., Yin, X., Cui, C., He, K., Wang, F., Chao, J., Li, T., Zuo, X., Li, A., Wang, L., et al. (2023) · Science Advances · doi:10.1126/sciadv.adf8263 · Cited by 13
Analysis of DNA Origami Nanostructures Using Capillary Electrophoresis
Hui, J., Majikes, J. M., & Riley, K. R. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c03641 · Cited by 13
Self-citationDNA‐Based Adaptive Plasmonic Logic Gates
Dong, J., Wang, M., Zhou, Y., Zhou, C., & Wang, Q. (2020) · Angewandte Chemie · doi:10.1002/ange.202006029 · Cited by 13
DNA‐Guided Assembly of Molecules, Materials, and Cells
Yang, D., Zhou, C., Gao, F., Wang, P., & Ke, Y. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900101 · Cited by 12
The program-size complexity of self-assembled paths
Meunier, P.-É., Regnault, D., & Woods, D. (2020) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3357713.3384263 · Cited by 12
Self-citationImplementing re-configurable biological computation with distributed multicellular consortia
Canadell, D., Ortiz-Vaquerizas, N., Mogas-Díez, S., de Nadal, E., Macía, J., & Posas, F. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1120 · Cited by 13
Leveraging autocatalytic reactions for chemical domain image classification
Arcadia, C. E., Dombroski, A., Oakley, K., Chen, S. L., Tann, H., Rose, C., Kim, E., Reda, S., Rubenstein, B. M., & Rosenstein, J. K. (2021) · Chemical Science · doi:10.1039/d0sc05860b · Cited by 12
Self‐assembled free‐floating nanomaterials from sequence‐defined polymers
Babi, J., Zhu, L., Lin, A., Uva, A., El‐Haddad, H., Peloewetse, A., & Tran, H. (2021) · Journal of Polymer Science · doi:10.1002/pol.20210366 · Cited by 12
Dhakal, N. P., Jiang, J., Guo, Y., & Peng, C. (2020) · Physical Review Applied · doi:10.1103/physrevapplied.13.014026 · Cited by 12
DNA Origami Nanostructure Detection and Yield Estimation Using Deep Learning
Chen, C., Nie, J., Ma, M., & Shi, X. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00533 · Cited by 11
AMP Aptamer Programs DNA Tile Cohesion without Canonical Base Pairing
Zhang, Z., Jin, J., Paluzzi, V. E., Jin, Z., Wen, Y., Huang, C. Z., Li, C. M., Mao, C., & Zuo, H. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c06260 · Cited by 11
Zhang, F., Dai, W., Zhang, M., Dong, H., & Zhang, X. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c06960 · Cited by 11
Suppressing high-dimensional crystallographic defects for ultra-scaled DNA arrays
Chen, Y., Yang, C. J., Zhu, Z., & Sun, W. (2022) · Nature Communications · doi:10.1038/s41467-022-30441-1 · Cited by 11
A bistable and reconfigurable molecular system with encodable bonds
Zhou, C., Yang, D., Sensale, S., Sharma, P., Wang, D., Yu, L., Arya, G., Ke, Y., & Wang, P. (2022) · Science Advances · doi:10.1126/sciadv.ade3003 · Cited by 11
A molecular assessment of the practical potential of DNA-based computation
Polak, R. E., & Keung, A. J. (2023) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2023.102940 · Cited by 11
Application and prospects of nucleic acid nanomaterials in tumor therapy
Lu, W., Chen, T., Xiao, D., Qin, X., Chen, Y., & Shi, S. (2023) · RSC Advances · doi:10.1039/d3ra04081j · Cited by 11
Statistical reprogramming of macroscopic self-assembly with dynamic boundaries
Çulha, U., Davidson, Z. S., Mastrangeli, M., & Sitti, M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2001272117 · Cited by 11
The art of molecular computing: Whence and whither
Gangadharan, S., & Raman, K. (2021) · BioEssays · doi:10.1002/bies.202100051 · Cited by 11
2D DNA lattices assembled from DX-coupled tiles
Zhang, W., Jiang, C., Guo, X., Baig, M. M. F. A., Ni, C., & Xiao, S. (2022) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2022.02.038 · Cited by 11
A Novel Algorithm for Solving the Prize Collecting Traveling Salesman Problem Based on DNA Computing
Wang, Z., Liang, K., Bao, X., & Wu, T.-H. (2023) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2023.3307458 · Cited by 10
Ershova, A., Minev, D., Corea-Dilbert, F. E., Yu, D., Deng, J., Fontana, W., & Shih, W. M. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c08205 · Cited by 10
DNA Self-Assembly: A Tool to Improve Biochemical Reaction Performance
Hu, Q., Yan, J., & Ren, K. (2024) · ACS Materials Letters · doi:10.1021/acsmaterialslett.4c01209 · Cited by 12
Nanotubes Growth by Self-Assembly of DNA Strands at Room Temperature
Bourdon, L., Afrose, S. P., Agarwal, S., Das, D., Singh, R., Cicco, A. D., Lévy, D., Yamada, A., Baigl, D., & Franco, E. (2025) · ACS Nano · doi:10.1021/acsnano.4c17516 · Cited by 10
Self-Stabilizing Self-Assembly
Jílek, M., Stránská, K., Somr, M., Kulich, M., Zeman, J., & Přeučil, L. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3191795 · Cited by 10
Hader, D., Koch, A., Patitz, M. J., & Sharp, M. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975994.159 · Cited by 10
Chen, Q., Guo, A., Liu, J., Peeters, F. M., & Sun, Q. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac2711 · Cited by 10
Demonstration of elementary functions via DNA algorithmic self-assembly
Raza, M. T., Tandon, A., Park, S., Lee, S.-J., Nguyen, T. B. N., Vu, T. H. N., Jo, S., Nam, Y., Jeon, S., Jeong, J., et al. (2021) · Nanoscale · doi:10.1039/d1nr05055a · Cited by 10
Perumal, A. S., Wang, Z., Ippoliti, G., van Delft, F. C. M. J. M., Lila Kari, A., & Nicolau, D. V. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac3883 · Cited by 11
Engineering protein activity into off-the-shelf DNA devices
Sekhon, H., & Loh, S. N. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100202 · Cited by 8
Graph Computation Using Algorithmic Self-Assembly of DNA Molecules
Xu, J., Chen, C., & Shi, X. (2022) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00120 · Cited by 9
Multi-micron crisscross structures from combinatorially assembled DNA-origami slatsPreprint
Wintersinger, C. M., Minev, D., Ershova, A., Sasaki, H. M., Gowri, G., Berengut, J. F., Corea-Dilbert, F. E., Yin, P., & Shih, W. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.06.475243 · Cited by 9
Self-citationKaygisiz, K., & Ulijn, R. V. (2025) · ChemSystemsChem · doi:10.1002/syst.202400075 · Cited by 13
Hierarchical core–shell DNA condensates enable programmable information storage and encryption
Chu, L., Wan, L., Wang, H., Wang, P., Deng, L., Gao, Y., Guo, P., He, L., & Han, D. (2025) · Nature Communications · doi:10.1038/s41467-025-67093-w · Cited by 10
miR-21 Responsive Nanocarrier Targeting Ovarian Cancer Cells
Han, L., Song, T., Wang, X., Luo, Y., Gu, C., Li, X. T., Wen, J., Wen, Z., & Shi, X. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.02.021 · Cited by 8
Instruction-responsive programmable assemblies with DNA origami block pieces
Wang, F., Shi, X., Chen, X., Deng, D., Li, S., Sun, S., Kou, Z., Xu, J. B., & Qiang, X. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1193 · Cited by 8
Simple Intrinsic Simulation of Cellular Automata in Oritatami Molecular Folding Model
Pchelina, D., Schabanel, N., Seki, S., & Ubukata, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61792-9_34 · Cited by 8
Out‐of‐Equilibrium (Supra)molecular Systems and Materials: An Introduction
Giuseppone, N., & Walther, A. (2021) · Wiley eBooks · doi:10.1002/9783527821990.ch1 · Cited by 8
Branching Crisscross Polymerization of Single-Stranded DNA Slats
Deng, J., Minev, D., Ershova, A., & Shih, W. M. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c00097 · Cited by 7
Multiscale Biofabrication: Integrating Additive Manufacturing with DNA‐Programmable Self‐Assembly
Jäkel, A. C., Heymann, M., & Simmel, F. C. (2022) · Advanced Biology · doi:10.1002/adbi.202200195 · Cited by 7
A new DNA-based model for finite field arithmetic
Jirón, I., Soto, S., Marín, S., Acosta, M., & Soto, I. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e02901 · Cited by 7
Dai, Y., Xu, W., Somoza, R. A., Welter, J. F., Caplan, A. I., & Liu, C.-C. (2020) · Angewandte Chemie · doi:10.1002/ange.202010648 · Cited by 7
Molecular computation at equilibrium via programmable entropy
Wang, B., Chalk, C., Doty, D. S., & Soloveichik, D. (2026) · Science Advances · doi:10.1126/sciadv.adx3969 · Cited by 6
Self-citationThree levels of information processing in the brain
Zhuravlev, A. V. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.104934 · Cited by 6
On geometric shape construction via growth operations
Almalki, N., & Michail, O. (2023) · Theoretical Computer Science · doi:10.1016/j.tcs.2023.114324 · Cited by 6
Park, S., Tandon, A., Cho, H. J., Raza, M. T., Lee, S. J., Chopade, P., Ha, T. H., & Park, S. H. (2019) · Nanotechnology · doi:10.1088/1361-6528/ab5472 · Cited by 6
Formal Semantics and Verification of Network-Based Biocomputation Circuits
Aluf-Medina, M., Korten, T., Raviv, A., Nicolau, D. V., & Kugler, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67067-2_21 · Cited by 6
MC NFV: Molecular Communication NFV in 6G Networks
Hofmann, P., Bassoli, R., Fitzek, F. H. P., & Martin Reisslein, B. (2022) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon53508.2022.9843087 · Cited by 6
Stability and Stabilization of DNA Nanostructures in Biomedical Applications
Chandrasekhar, S., Prakash, P. S., & Schmidt, T. L. (2022) · Wiley eBooks · doi:10.1002/9781119682561.ch16 · Cited by 6
Toward high-density streptavidin arrays on DNA origami nanostructures
Rabbe, L., Tomm, E., Grundmeier, G., & Keller, A. (2025) · RSC Advances · doi:10.1039/d5ra03393d · Cited by 5
A recursive enzymatic competition network capable of multitask molecular information processing
Ghosh, S., Baltussen, M. G., Knox, A. C., Haije, R., Duez, Q., Tsitsimeli, A. T., Chak, M. H., Beves, J. E., & Huck, W. T. S. (2025) · Nature Chemistry · doi:10.1038/s41557-025-01981-y · Cited by 8
Growth of Porous Ag@AuCu Trimetal Nanoplates Assisted by Self-Assembly
Zhang, W., Luoshan, M., Wang, P., Huang, C., Wang, Q., Ding, S., & Zhou, L. (2020) · Nanomaterials · doi:10.3390/nano10112207 · Cited by 5
The Complexity of Growing a Graph
Mertzios, G. B., Michail, O., Skretas, G., Spirakis, P. G., & Theofilatos, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22050-0_9 · Cited by 5
Catalyst-Based Biomolecular Logic Gates
Winston, D. S., & Boehr, D. D. (2022) · Catalysts · doi:10.3390/catal12070712 · Cited by 5
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Cai, B., Xiao, R., Sun, Y., Liu, L., & Li, Z. (2025) · Small Methods · doi:10.1002/smtd.202401455 · Cited by 7
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Guntoro, J., Qureshi, B., & Ouldridge, T. E. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0245687 · Cited by 5
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Sellami, H., Cazenille, L., Fujii, T., Hagiya, M., Aubert-Kato, N., & Genot, A. J. (2020) · Micromachines · doi:10.3390/mi11090881 · Cited by 5
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Programmable Assembly of DNA-protein Hybrid Structures
Li, X., Yang, D., Shen, L., Xu, F., & Wang, P. (2019) · Chemical Research in Chinese Universities · doi:10.1007/s40242-019-0038-x · Cited by 5
Construction of one-dimensional random walk lattices using DNA algorithmic self-assembly
Raza, M. T., Tandon, A., Son, J., Park, S., Lee, S.-J., Cho, H., Ha, T. H., & Park, S. H. (2020) · AIP Advances · doi:10.1063/1.5121827 · Cited by 5
Toward Smart Information Processing with Synthetic DNA Molecules
Chu, J., Zhang, Y., Wang, F., & Liu, H. (2021) · Macromolecular Rapid Communications · doi:10.1002/marc.202100084 · Cited by 6
Deep convolutional and fully-connected DNA neural networks
Liu, X., Zheng, Z., Liu, P., Yang, Z., Hu, H., Wu, M., Lou, X., Xia, F., Tao, K., Li, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65618-x · Cited by 4
On the effects of hierarchical self-assembly for reducing program-size complexity
Cannon, S., Demaine, E. D., Demaine, M. L., Eisenstat, S., Furcy, D., Patitz, M. J., Schweller, R., Summers, S. M., & Winslow, A. (2021) · Theoretical Computer Science · doi:10.1016/j.tcs.2021.09.011 · Cited by 4
Surface-assisted self-assembly of 2D, DNA binary crystals
Liu, L., Mao, D., Li, Z., Zheng, M., He, K., & Mao, C. (2023) · Nanoscale · doi:10.1039/d3nr01187a · Cited by 4
Hader, D., & Patitz, M. J. (2024) · Algorithmica · doi:10.1007/s00453-024-01219-2 · Cited by 4
Mougkogiannis, P., & Adamatzky, A. (2024) · Molecules · doi:10.3390/molecules29194700 · Cited by 4
Dhami, I., Thadke, S. A., & Ly, D. H. (2022) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.2c01560 · Cited by 5
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Hu, X., Xu, B., Chen, M., Li, K., Xiao, Y., Liang, S., Zhang, C., Ma, H., & Song, H. (2024) · Journal of Biosafety and Biosecurity · doi:10.1016/j.jobb.2024.03.001 · Cited by 5
Emanuelson, C., Bardhan, A., Ankenbruck, N., Boette, J., & Deiters, A. (2025) · ACS Chemical Biology · doi:10.1021/acschembio.5c00333 · Cited by 3
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Zhang, L., Sha, R., & Chaikin, P. M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2500160122 · Cited by 3
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Fan, H., Li, J., Wang, L., Fan, C., & Liu, H. (2020) · Acta Physica Sinica · doi:10.7498/aps.70.20201438 · Cited by 3
An integer linear programming approach to solving the Eternity Puzzle
Burkardt, J. V., & Garvie, M. R. (2023) · Theoretical Computer Science · doi:10.1016/j.tcs.2023.114138 · Cited by 3
Self-assembling DNA recognizes patterns
Phillips, A. (2024) · Nature · doi:10.1038/d41586-023-03997-1 · Cited by 3
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Yu, D., Fan, X., & Chen, Z. (2024) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2024.100556 · Cited by 3
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Davendra, D. D., & Bialic-Davendra, M. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.78197 · Cited by 3
Murphy, C., Cao, Y., Sepúlveda, N., & Li, W. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246453 · Cited by 3
Programming the Nucleation of DNA Brick Self‐Assembly with a Seeding Strand
Zhang, Y., Reinhardt, A., Wang, P., Song, J., & Ke, Y. (2020) · Angewandte Chemie · doi:10.1002/ange.201915063 · Cited by 5
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Bai, T., & Wei, B. (2019) · Nanoscale · doi:10.1039/c9nr07196b · Cited by 3
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Chen, C., Xu, J., Zhang, Y., Li, X., & Shi, X. (2021) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2021.3095157 · Cited by 3
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Függer, M., Nowak, T., & Thuillier, K. (2025) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2025.103666 · Cited by 2
Raza, M. T., Tandon, A., Park, S., Lee, S.-J., Nguyen, T. B. N., Vu, T. H. N., & Park, S. H. (2023) · Nanotechnology · doi:10.1088/1361-6528/acc1ed · Cited by 2
Simulation of Multiple Stages in Single Bin Active Tile Self-assembly
Cirlos, S. C., Gomez, T., Grizzell, E., Rodriguez, A., Schweller, R., & Wylie, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34034-5_11 · Cited by 2
DNA Nanotechnology Out of Equilibrium
Simmel, F. C. (2023) · Natural computing series · doi:10.1007/978-981-19-9891-1_2 · Cited by 2
Matrix-Chain Multiplication Based on Combinatorial Allosteric DNA Strand Displacement
Guo, H., Wang, M. Z., & Zheng, X. (2024) · IEEE Access · doi:10.1109/access.2024.3412919 · Cited by 2
Vertical DNA Nanostructure Arrays: Facilitating Functionalization on Macro-Scale Surfaces
Kwon, H., Shin, J., Sun, S., Zhu, R., Stainer, S., Hinterdorfer, P., Cho, S., Kim, D., & Oh, Y. J. (2025) · ACS Nano · doi:10.1021/acsnano.5c03100 · Cited by 2
DNA tile self-assembly driven by antibody-mediated four-way branch migration
Cui, X., Liu, Y., & Zhang, Q. (2022) · The Analyst · doi:10.1039/d1an02273c · Cited by 2
DNA-like Materials Could Open New Computing Frontiers
Chandler, D. L. (2020) · IEEE Pulse · doi:10.1109/mpuls.2020.2993685 · Cited by 2
DNAQL: a query language for DNA sticker complexes
Brijder, R., Gillis, J. J. M., & den Bussche, J. V. (2021) · Natural Computing · doi:10.1007/s11047-020-09839-7 · Cited by 2
Zhang, D., Huang, X.-R., Huang, J.-G., Li, Y., & Cai, J. (2021) · Journal of Porphyrins and Phthalocyanines · doi:10.1142/s1088424621500280 · Cited by 2
Metadata
- Authors
- Woods, D., Doty, D., Myhrvold, C., Hui, J., Zhou, F., Yin, P., & Winfree, E.
- Affiliations
- California Institute of Technology (US)
- Harvard University (US)
- Corresponding author
- Woods, Damien
- Doty, David
- Winfree, Erik
- Venue
- Nature
- Published
- 2019-03
- Volume
- 567
- Issue
- 7748
- Pages
- 366-372
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2924487690
- Cited by
- 295
- References
- 36
- Citations by year
- 2026: 22 · 2025: 38 · 2024: 35 · 2023: 49 · 2022: 47 · 2021: 50 · 2020: 36 · 2019: 18
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-08-02)
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Theoretical Computer Science › DNA and Biological Computing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Keywords
- DNA self-assembly, DNA nanotechnology, algorithmic self-assembly, sorting algorithms, cellular automata simulation, leader election, counting, error rate
- MeSH terms
- Algorithms, DNA, Reproducibility of Results, Nanotechnology
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