The Winner-Take-All Neural Network Based on DNA Strand Displacement

Bin WANG; Ya LI; Hongwei ZHAO

doi:10.11999/JEIT200579

Volume 43 Issue 8

Aug. 2021

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Bin WANG, Ya LI, Hongwei ZHAO. The Winner-Take-All Neural Network Based on DNA Strand Displacement[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2430-2438. doi: 10.11999/JEIT200579

Citation:

Bin WANG, Ya LI, Hongwei ZHAO. The Winner-Take-All Neural Network Based on DNA Strand Displacement[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2430-2438. doi: 10.11999/JEIT200579

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The Winner-Take-All Neural Network Based on DNA Strand Displacement

doi: 10.11999/JEIT200579 cstr: 32379.14.JEIT200579

Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China

Funds: The National Key R&D Program of China (2018YFC0910500), The National Natural Science Foundation of China (61425002, 61751203, 61772100, 61972266, 61802040, 61672121), The Natural Science Foundation of Liaoning of Province (20180551241, 2019-ZD-0567)

Received Date: 2020-07-15
Rev Recd Date: 2020-11-10

Available Online: 2020-11-23

Publish Date: 2021-08-10

Abstract

Abstract

DNA strand displacement technology is widely used in biological computing, and it has excellent performance in computing power and information processing. However, the use of DNA Strand Displacement (DSD) technology in some calculations, such as signal amplification, restoration, and comparison, not only increases the number of DNA strands, but also brings additional calculation costs. Therefore, in order to reduce the number of DNA strands used, a Winner-Take-All (WTA) neural network based on DNA strand displacement is constructed. Firstly, the logic operations AND, NAND, and OR are realized through neurons, and the linear inseparable problem is solved by cascading them into a WTA neural network. By comparing with the results with others, the effectiveness of the method is proved, and stable and intuitive results are obtained in Visual DSD (DNA Strand Displacement). Then, in order to test the scalability of the neuron cascade, a three-person voter is designed and the scientists are classified. The paper shows how the molecular system demonstrates the ability to think in a similar way to the brain, and finally proves the accuracy is higher than other methods.
- DNA Strand Displacement (DSD),
- Winner-Take-All (WTA) neural network,
- Logic operation,
- Visual DSD

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References(25)

References

[1]	ADLEMAN L M. Molecular computation of solutions to combinatorial problems[J]. Science, 1994, 266(5187): 1021–1024. doi: 10.1126/science.7973651
[2]	LI Wei, YANG Yang, YAN Hao, et al. Three-input majority logic gate and multiple input logic circuit based on DNA strand displacement[J]. Nano Letters, 2013, 13(6): 2980–2988. doi: 10.1021/nl4016107
[3]	THUBAGERE A J, THACHUK C, BERLEANT J, et al. Compiler-aided systematic construction of large-scale DNA strand displacement circuits using unpurified components[J]. Nature Communications, 2017, 8: 14373. doi: 10.1038/ncomms14373
[4]	GREEN S J, LUBRICH D, and TURBERFIELD A J. DNA hairpins: fuel for autonomous DNA devices[J]. Biophysical Journal, 2006, 91(8): 2966–2975. doi: 10.1529/biophysj.106.084681
[5]	ZHAO Yunbin, LIU Yuan, ZHENG Xuedong, et al. Half adder and half subtractor logic gates based on nicking enzymes[J]. Molecular Systems Design & Engineering, 2019, 4(6): 1103–1113. doi: 10.1039/C9ME00090A
[6]	殷志祥, 唐震, 张强, 等. 基于DNA折纸基底的与非门计算模型[J]. 电子与信息学报, 2020, 42(6): 1355–1364. doi: 10.11999/JEIT190825 YIN Zhixiang, TANG Zhen, ZHANG Qiang, et al. NAND gate computational model based on the DNA origami template[J]. Journal of Electronics &Information Technology, 2020, 42(6): 1355–1364. doi: 10.11999/JEIT190825
[7]	孙军伟, 李智, 王延峰. 基于DNA链置换的三级联组合分子逻辑电路设计[J]. 电子与信息学报, 2020, 42(6): 1401–1409. doi: 10.11999/JEIT190847 SUN Junwei, LI Zhi, and WANG Yanfeng. Design of three-cascade combinatorial molecular logic circuit based on DNA strand displacement[J]. Journal of Electronics &Information Technology, 2020, 42(6): 1401–1409. doi: 10.11999/JEIT190847
[8]	LILIENTHAL S, KLEIN M, ORBACH R, et al. Continuous variables logic via coupled automata using a DNAzyme cascade with feedback[J]. Chemical Science, 2017, 8(3): 2161–2168. doi: 10.1039/C6SC03892A
[9]	WANG Bin, XIE Yingjie, ZHOU Shihua, et al. Correcting errors in image encryption based on DNA coding[J]. Molecules, 2018, 23(8): 1878. doi: 10.3390/molecules23081878
[10]	WANG Bin, ZHANG Qiang, and WEI Xiaopeng. Tabu variable neighborhood search for designing DNA barcodes[J]. IEEE Transactions on NanoBioscience, 2020, 19(1): 127–131. doi: 10.1109/TNB.2019.2942036
[11]	SONG Tianqi, GOPALKRISHNAN N, ESHRA A, et al. Improving the performance of DNA strand displacement circuits by shadow cancellation[J]. ACS Nano, 2018, 12(11): 11689–11697. doi: 10.1021/acsnano.8b07394
[12]	许鹏, 方刚, 石晓龙, 等. DNA存储及其研究进展[J]. 电子与信息学报, 2020, 42(6): 1326–1331. doi: 10.11999/JEIT190863 XU Peng, FANG Gang, SHI Xiaolong, et al. DNA storage and its research progress[J]. Journal of Electronics &Information Technology, 2020, 42(6): 1326–1331. doi: 10.11999/JEIT190863
[13]	SEELIG G, SOLOVEICHIK D, ZHANG D Y, et al. Enzyme-free nucleic acid logic circuits[J]. Science, 2006, 314(5805): 1585–1588. doi: 10.1126/science.1132493
[14]	ZHANG D Y, TURBERFIELD A J, YURKE B, et al. Engineering entropy-driven reactions and networks catalyzed by DNA[J]. Science, 2007, 318(5853): 1121–1125. doi: 10.1126/science.1148532
[15]	LAKIN M R and STEFANOVIC D. Supervised learning in adaptive DNA strand displacement networks[J]. ACS Synthetic Biology, 2016, 5(8): 885–897. doi: 10.1021/acssynbio.6b00009
[16]	SONG Tianqi, GARG S, MOKHTAR R, et al. Analog computation by DNA strand displacement circuits[J]. ACS Synthetic Biology, 2016, 5(8): 898–912. doi: 10.1021/acssynbio.6b00144
[17]	QIAN Lulu, WINFREE E, and BRUCK J. Neural network computation with DNA strand displacement cascades[J]. Nature, 2011, 475(7356): 368–372. doi: 10.1038/nature10262
[18]	GENOT A J, FUJII T, and RONDELEZ Y. Scaling down DNA circuits with competitive neural networks[J]. Journal of the Royal Society Interface, 2013, 10(85): 20130212. doi: 10.1098/rsif.2013.0212
[19]	SHI Xiaolong, WANG Zhiyu, DENG Chenyan, et al. A novel bio-sensor based on DNA strand displacement[J]. PLoS One, 2014, 9(10): e108856. doi: 10.1371/journal.pone.0108856
[20]	CHERRY K M and QIAN Lulu. Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks[J]. Nature, 2018, 559(7714): 370–376. doi: 10.1038/s41586-018-0289-6
[21]	LAKIN M R, YOUSSEF S, POLO F, et al. Visual DSD: A design and analysis tool for DNA strand displacement systems[J]. Bioinformatics, 2011, 27(22): 3211–3213. doi: 10.1093/bioinformatics/btr543
[22]	WANG Yanfeng, ZHANG Wenwen, LI Xing, et al. Molecular logic gates based on localized DNA strand displacement[J]. Journal of Computational and Theoretical Nanoscience, 2016, 13(6): 3948–3952. doi: 10.1166/jctn.2016.5231
[23]	SONG Tianqi, GARG S, MOKHTAR R, et al. Design and analysis of compact DNA strand displacement circuits for analog computation using autocatalytic amplifiers[J]. ACS Synthetic Biology, 2018, 7(1): 46–53. doi: 10.1021/acssynbio.6b00390
[24]	YANG Jing, WU Ranfeng, LI Yifan, et al. Entropy-driven DNA logic circuits regulated by DNAzyme[J]. Nucleic Acids Research, 2018, 46(16): 8532–8541. doi: 10.1093/nar/gky663
[25]	ESHRA A, SHAH S, SONG Tianqi, et al. Renewable DNA hairpin-based logic circuits[J]. IEEE Transactions on Nanotechnology, 2019, 18: 252–259. doi: 10.1109/TNANO.2019.2896189