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Molecular solutions for minimum and exact cover problems in the tile assembly model

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Abstract

The tile assembly model is a novel biological computing model where information is encoded in DNA tiles. It is an efficient way to solve NP-complete problems due to its scalability and parallelism. In this paper, we apply the tile assembly model to solve the minimum and exact set cover problems, which are well-known NP-complete problems. To solve the minimum set cover problem, we design a MinSetCover system composed of three parts, i.e., the seed configuration subsystem, the nondeterministic choice subsystem, and the detection subsystem. Moreover, we improve the MinSetCover system and propose a MinExactSetCover system for solving the problem of exact cover by 3-sets. Finally we analyze the computation complexity and perform a simulation experiment to verify the effectiveness and correctness of the proposed systems.

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References

  1. Chang WL, Lin KW, Chen JC et al (2012) Molecular solutions of the RSA public-key cryptosystem on a DNA-based computer. J Supercomput 61(3):642–672

  2. Chang WL (2012) Fast parallel DNA-based algorithm for molecular computation: quadratic congruence and factoring integers. IEEE Trans Nanobiosci 11(1):62–69

    Article  Google Scholar 

  3. Chang WL, Huang SC, Lin WC et al (2011) Fast parallel DNA-based algorithms for molecular computation: discrete logarithm. J Supercomput 56:129–163

    Article  Google Scholar 

  4. Jin X, Xiaoli Q, Yan Y et al (2011) An unenumerative DNA computing model for vertex coloring problem. IEEE Trans Nanobiosci 10(2):94–98

    Article  Google Scholar 

  5. Li K, Zou S, Xu J (2008) Fast parallel molecular algorithms for DNA-based computation: solving the elliptic curve discrete logarithm problem over GF(2n). J Biomed Biotechnol 1:1–10

  6. Zhou Xu, Li Kenli et al (2011) A novel approach for the classical Ramsey number problem on DNA-based super-computing. Match Commun Math Comput Chem 66(1):347–370

    MathSciNet  Google Scholar 

  7. Daniel M, Alfonso R-P, Petr S (2011) On the scalability of biocomputing algorithms: the case of the maximum clique problem. Theor Comput Sci 412(51):7075–7086

    Article  MATH  Google Scholar 

  8. Bakar RAB, Watada J, Pedrycz W (2008) DNA approach to solve clustering problem based on a mutual order. Biosystems 91(1):1–12

    Article  Google Scholar 

  9. Ikno K, Junzo W, Wutikd P et al (2012) Pattern clustering with statistical methods using a DNA-based algorithm. IEEE Trans Nanobiosci 11(2):100–110

    Article  Google Scholar 

  10. Ikno K, Junzo W (2009) Decision making with an interpretive structural modeling method using a DNA-based algorithm. IEEE Trans Nanobiosci 8(2):181–191

    Article  Google Scholar 

  11. Winfree E (1998) Design and self-assembly of two-dimensional DNA crystals. Nature 394(8):1223–1226

    Google Scholar 

  12. Adleman LM, Cheng Q, Goel A et al (2001) Running time and program size for self-assembled squares. In: Proceedings of the thirty-third annual ACM symposium on theory of computing, STOC 2001, Hersonissos, Greece. ACM, pp 740–748

  13. Summers S (2012) Reducing tile complexity for the self-assembly of scaled shapes through temperature programming. Algorithmica 63:1–20

    Article  MathSciNet  Google Scholar 

  14. Woods D, Chen HL, Goodfriend S et al (2013) Active self-assembly of algorithmic shapes and patterns in polylogarithmic time. In: Innovations in theoretical computer science, ITCS 2013, Berkeley, California, January 10–12

  15. Brun Y (2006) Arithmetic computation in the tile assembly model: addition and multiplication. Theor Comput Sci 378:17–31

    Article  MathSciNet  Google Scholar 

  16. Brun Y (2008) Solving NP-complete problems in the tile assembly model. Theor Comput Sci 395(1):31–46

    Article  MATH  MathSciNet  Google Scholar 

  17. Brun Y (2008) Nondeterministic polynomial time factoring in the tile assembly model. Theor Comput Sci 395(1):3–23

    Article  MATH  MathSciNet  Google Scholar 

  18. Brun Y (2008) Solving satisfiability in the tile assembly model with a constant-size tile set. J Algorithms 63(4):151–166

    Article  MATH  MathSciNet  Google Scholar 

  19. Zhang XC, Niu Y et al (2009) Application of DNA self-assembly on graph coloring problem. J Comput Theor Nanosci 6(5):1067–1074

    Article  Google Scholar 

  20. Cheng Z, Huang YF et al (2009) Algorithm of solving the subset-product problem based on DNA tile self-assembly. J Comput Theor Nanosci 6(5):1161–1169

    Article  Google Scholar 

  21. Guangzhao C, Cuiling L et al (2009) Application of DNA self-assembly on maximum clique problem. Adv Intell Soft Comput 116:359–368

    Article  Google Scholar 

  22. Jie L, Lei Y, Kenli L (2008) An \(O\)(1.414\(^{n})\) volume molecular solutions for the exact cover problem on DNA-based supercomputing. J Inf Comput Sci 5(1):153–162

    Google Scholar 

  23. Chang WL, Guo M (2003) Solving the set-cover problem and the problem of exact cover by 3-sets in the Adleman–Lipton model. BioSystems 72(2):263–275

    Article  Google Scholar 

  24. Fan Wu, Li Kenli, Sallam Ahmed et al (2013) A molecular solution for minimum vertex cover problem in tile assembly model. J Supercomput 66:148–169

    Article  Google Scholar 

  25. Winfree E The xgrow simulator. http://dna.caltech.edu/Xgrow/

  26. Tsai CC, Huang HC, Lin SC (2011) FPGA-based parallel DNA algorithm for optimal configurations of an omnidirectional mobile service robot performing fire extinguishment. IEEE Trans Ind Electron 58(3):1016–1026

    Article  Google Scholar 

  27. Lulu Q, Erik W, Bruck J (2011) Neural network computation with DNA strand displacement cascades. Nature 475:368–372

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the key Project of National Natural Science Foundation of China under grant 61133005, the Project of National Natural Science Foundation of China under grant 61173013 and 61202109, the Project of the Office of Education in Zhejiang Province under grant Y201226110.

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Correspondence to Xu Zhou.

Appendix

Appendix

The simulation results by running the Xgrow simulator for solving our problem above (Figs. 20, 21).

Fig. 20
figure 20

The results of simulating the MinSetCover system by Xgrow

Fig. 21
figure 21

The results of simulating the MinExtractSetCover system by Xgrow

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Zhou, X., Zhou, Y., Li, K. et al. Molecular solutions for minimum and exact cover problems in the tile assembly model. J Supercomput 69, 976–1005 (2014). https://doi.org/10.1007/s11227-014-1222-x

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