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H-ACO with Consecutive Bases Pairing Constraint for Designing DNA Sequences

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Abstract

DNA computing is a novel computing method that does not rely on traditional computers. The design of DNA sequences is a crucial step in DNA computing, and the quality of the sequence design directly affects the results of DNA computing. In this paper, a new constraint called the consecutive base pairing constraint is proposed to limit specific base pairings in DNA sequence design. Additionally, to improve the efficiency and capability of DNA sequence design, the Hierarchy-ant colony (H-ACO) algorithm is introduced, which combines the features of multiple algorithms and optimizes discrete numerical calculations. Experimental results show that the H-ACO algorithm performs well in DNA sequence design. Finally, this paper compares a series of constraint values and NUPACK simulation data with previous design results, and the DNA sequence set designed in this paper has more advantages.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Li X, Wang B, Lv H et al (2020) Constraining DNA sequences with a triplet-bases unpaired. IEEE Trans NanoBiosci 19(2):299–307. https://doi.org/10.1109/TNB.2020.2971644

    Article  Google Scholar 

  2. Adleman LM (1994) Molecular computation of solutions to combinatorial problems. Science 266(5187):1021–1024. https://doi.org/10.1126/science.7973651

    Article  CAS  PubMed  Google Scholar 

  3. Sreeja CS, Misbahuddin M, Hashim NPM (2014) DNA for information security: a survey on DNA computing and a pseudo DNA method based on central dogma of molecular biology. Int Conf Comput Commun Technol IEEE 2014:1–6. https://doi.org/10.1109/ICCCT2.2014.7066757

    Article  Google Scholar 

  4. Namasudra S, Chakraborty R, Majumder A et al (2020) Securing multimedia by using DNA-based encryption in the cloud computing environment. ACM Trans Multimed Comput Commun Appl (TOMM) 16(3s):1–19. https://doi.org/10.1145/3392665

  5. Zhang X, Zhou Z, Niu Y (2018) An image encryption method based on the Feistel network and dynamic DNA encoding. IEEE Photonics J 10(4):1–14. https://doi.org/10.1109/JPHOT.2018.2859257

    Article  Google Scholar 

  6. Babaei M (2013) A novel text and image encryption method based on chaos theory and DNA computing. Nat Comput 12(1):101–107. https://doi.org/10.1007/s11047-012-9334-9

    Article  CAS  Google Scholar 

  7. Kim CS, Gulati S, Ayub M et al (2016) A novel PCR error correction algorithm for cell-free DNA next generation sequencing data using high performance computing. Eur J Cancer 1(61):S186. https://doi.org/10.1016/S0959-8049(16)61660-X

    Article  Google Scholar 

  8. Uil TG, Haisma HJ, Rots MG (2003) Therapeutic modulation of endogenous gene function by agents with designed DNA-sequence specificities. Nucleic Acids Res 31(21):6064–6078. https://doi.org/10.1093/nar/gkg815

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhang M, Sabharwal CL, Tao W et al (2004) Interactive DNA sequence and structure design for DNA nanoapplications. IEEE Trans NanoBiosci 3(4):286–292. https://doi.org/10.1109/TNB.2004.837918

    Article  Google Scholar 

  10. Pinheiro AV, Han D, Shih WM et al (2011) Challenges and opportunities for structural DNA nanotechnology. Nat Nanotechnol 6(12):763–772. https://doi.org/10.1038/nnano.2011.187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Liu W, Wang S, Gao L et al (2003) DNA sequence design based on template strategy. J Chem Inf Comput Sci 43(6):2014–2018. https://doi.org/10.1021/ci025645s

    Article  CAS  PubMed  Google Scholar 

  12. Shin SY, Lee IH, Kim D et al (2005) Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing. IEEE T Evol Comput 9(2):143–158. https://doi.org/10.1109/TEVC.2005.844166

    Article  Google Scholar 

  13. Kawashimo S, Ono H, Sadakane K et al (2006) DNA sequence design by dynamic neighborhood searches. International workshop on DNA-based computers. Springer, Berlin, pp 157–171. https://doi.org/10.1007/11925903

  14. Zhang K, Xu J, Geng X et al (2008) Improved taboo search algorithm for designing DNA sequences. Prog Nat Sci 18(5):623–627. https://doi.org/10.1016/j.pnsc.2008.01.005

    Article  CAS  Google Scholar 

  15. Xiao JH, Zhang XY, Xu J (2012) A membrane evolutionary algorithm for DNA sequence design in DNA computing. Chin Sci Bull 57:698–706. https://doi.org/10.1007/s11434-011-4928-7

    Article  CAS  Google Scholar 

  16. Chaves-Gonzalez JM, Vega-Rodriguez MA (2014) DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm. Biosystems 116:49–64. https://doi.org/10.1016/j.biosystems.2013.12.005

    Article  CAS  PubMed  Google Scholar 

  17. Chaves-González JM, Vega-Rodríguez MA, Granado-Criado JM (2013) A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design. Eng Appl Artif Intell 26(9):2045–2057. https://doi.org/10.1016/j.engappai.2013.04.011

    Article  Google Scholar 

  18. Yang G, Wang B, Zheng X et al (2017) IWO algorithm based on niche crowding for DNA sequence design. Interdiscip Sci Comput Life Sci 9:341–349. https://doi.org/10.1007/s12539-016-0160-0

    Article  CAS  Google Scholar 

  19. Li X, Wei Z, Wang B et al (2021) Stable DNA sequence over close-ending and pairing sequences constraint. Front Genet 12:644484. https://doi.org/10.3389/fgene.2021.644484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhu D, Huang Z, Liao S et al (2023) Improved bare bones particle swarm optimization for DNA sequence design. IEEE Trans NanoBiosci 22:603–613. https://doi.org/10.1109/TNB.2022.3220795

    Article  CAS  Google Scholar 

  21. Azizi M (2021) Atomic orbital search: a novel metaheuristic algorithm. Appl Math Model 93:657–683. https://doi.org/10.1016/j.apm.2020.12.021

    Article  Google Scholar 

  22. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell M 1(4):28–39. https://doi.org/10.1109/MCI.2006.329691

    Article  Google Scholar 

  23. Zhu D, Wang S, Zhou C et al (2024) Human memory optimization algorithm: a memory-inspired optimizer for global optimization problems. Expert Syst Appl 237:121597. https://doi.org/10.1016/j.eswa.2023.121597

    Article  Google Scholar 

  24. Zhu D, Wang S, Zhou C et al (2023) Manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems. Appl Soft Comput 2023:110561. https://doi.org/10.1016/j.asoc.2023.110561

    Article  Google Scholar 

  25. Zadeh JN, Steenberg CD, Bois JS et al (2011) NUPACK: analysis and design of nucleic acid systems. J Comput Chem 32(1):170–173. https://doi.org/10.1002/jcc.21596

    Article  CAS  PubMed  Google Scholar 

  26. Mrevlishvili GM, Svintradze DV (2005) Complex between triple helix of collagen and double helix of DNA in aqueous solution. Int J Biol Macromol 35(5):243–245. https://doi.org/10.1016/j.ijbiomac.2005.02.004

    Article  CAS  PubMed  Google Scholar 

  27. Yamamoto M, Del-Claro K (2008) Natural history and foraging behavior of the carpenter ant Camponotus sericeiventris Guérin, 1838 (Formicinae, Campotonini) in the Brazilian tropical savanna. Acta Ethol 11(2):55–65. https://doi.org/10.1007/s10211-008-0041-6

    Article  Google Scholar 

  28. Karimkashi S, Kishk AA (2010) Invasive weed optimization and its features in electromagnetics. IEEE Trans Antennas Propag 58(4):1269–1278. https://doi.org/10.1109/TAP.2010.2041163

    Article  Google Scholar 

  29. Fan D, Wang J, Wang E et al (2020) Propelling DNA computing with materials’ power: Recent advancements in innovative DNA logic computing systems and smart bio-applications. Adv Sci 7(24):2001766. https://doi.org/10.1002/advs.202001766

    Article  CAS  Google Scholar 

  30. Liang Z, Qin Q, Zhou C (2022) An image encryption algorithm based on Fibonacci Q-matrix and genetic algorithm. Neural Comput Appl 34(21):19313–19341. https://doi.org/10.1007/s00521-022-07493-x

    Article  Google Scholar 

  31. Wang B, Zheng X, Zhou S et al (2017) Constructing DNA barcode sets based on particle swarm optimization. IEEE/ACM Trans Comput Biol Bioinform 15(3):999–1002. https://doi.org/10.1109/TCBB.2017.2679004

    Article  PubMed  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 62272418, 62102058), Basic public welfare research program of Zhejiang Province (No. LGG18E050011).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China

    Xuwei Yang, Donglin Zhu & Changjun Zhou

  2. Jinhua Polytechnic, Jinhua, 321000, China

    Can Yang

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  1. Xuwei Yang
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  2. Donglin Zhu
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  3. Can Yang
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  4. Changjun Zhou
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Contributions

Xuwei and Donglin completed the experiment and writing of the full manuscript, Changjun provided algorithm analysis, and Can provided experimental analysis. All the authors approved the manuscript.

Corresponding author

Correspondence to Changjun Zhou.

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Yang, X., Zhu, D., Yang, C. et al. H-ACO with Consecutive Bases Pairing Constraint for Designing DNA Sequences. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00614-1

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