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Hybrid multiobjective metaheuristics for the design of reliable DNA libraries

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Abstract

DNA-based technologies, such as nanotechnology, DNA sequencing or DNA computing, have grown significantly in recent years. The inherent properties presented in DNA molecules (storage density, parallelism possibilities, energy efficiency, etc) make these particles unique computational elements. However, DNA libraries used for computation have to fulfill strict biochemical properties to avoid undesirable reactions, because those reactions usually lead to incorrect calculations. DNA sequence design is an NP-hard problem which involves several heterogeneous and conflicting biochemical design criteria which may produce some difficulties for traditional optimization methods. In this paper, we propose a multiobjective evolutionary algorithm which is hybridized with a local search heuristics specially designed for the problem. The proposal is a multiobjective variant of the teaching–learning-based optimization algorithm. On the other hand, with the aim of ensuring the performance of our proposal, we have made comparisons with the well-known fast-non dominated sorting genetic algorithm and strength Pareto evolutionary algorithm 2, and other approaches published in the literature. After performing diverse comparisons, we can conclude that our hybrid approach is able to obtain very promising DNA sequences suitable for computation which outperform in reliability other results generated with other existing sequence design techniques published in the literature.

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References

  • Adleman, L.M.: Molecular computation of solutions to combinatorial problems. Science 266, 1021–1024 (1994)

    Article  Google Scholar 

  • Arita, M., Kobayashi, S.: DNA sequence design using templates. New Gener. Comput. 20, 263–277 (2002)

    Article  MATH  Google Scholar 

  • Arita, M., Nishikawa, A., Hagiya, M., Komiya, K., Gouzu, H., Sakamoto, K.: Improving sequence design for DNA computing. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 875–882 (2000)

  • Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)

    Article  Google Scholar 

  • Brenneman, A., Condon, A.E.: Strand design for biomolecular computation. Theor. Comp. Sci. 287, 39–58 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • Cervantes-Salido, V.M., Jaime, O., Brizuela, C.A., Martínez-Perez, I.M.: Improving the design of sequences for DNA computing: a multiobjective evolutionary approach. Appl. Soft Comput. 13, 4594–4607 (2013)

    Article  Google Scholar 

  • Chaves-Gonzalez, J.M., Vega-Rodriguez, M.A., Granado-Criado, J.M.: A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design. Eng. Appl. Artif. Intell. 26(9), 2045–2057 (2013)

    Article  Google Scholar 

  • Chaves-Gonzalez, J.M., Vega-Rodriguez, M.A.: A multiobjective approach based on the behaviour of fireflies to generate reliable DNA sequences for molecular computing. Appl. Math. Comput. 227, 291–308 (2014a)

    Article  MathSciNet  Google Scholar 

  • Chaves-Gonzalez, J.M., Vega-Rodriguez, M.A.: DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm. BioSystems 116, 49–64 (2014b)

    Article  Google Scholar 

  • Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic algorithms and evolutionary computation. Springer, Berlin (2002)

    Book  MATH  Google Scholar 

  • Cui, G., Li, X.: The optimization of DNA encodings based on modified PSO/GA algorithm. In: Proceedings of International Conference on Computer Design and Applications, pp. 609–614 (2010)

  • Deaton, R., Chen, J., Bi, H., Garzon, M., Rubin, H., Wood, D.H.: A PCR-based protocol for in vitro selection of noncrosshybridizing olgionucleotides. In: Proceedings of the 8th International Workshop DNA Based Computers, pp. 196–204 (2002a)

  • Deaton, R., Chen, J., Bi, H., Rose, J.A.: Asoftware tool for generating noncrosshybridization libraries of DNA oligonucleotides. In: Proceedings of the 8th International Workshop DNA Based Computers, pp. 252–261 (2002b)

  • Deaton, R., Garzon, M., Murphy, R.C., Rose, J.A., Franceschetti, D.R., Stevens Jr, S.E.: Reliability and efficiency of a DNA-based computation. Phy. Rev. Lett. 80(2), 417–420 (1998)

    Article  Google Scholar 

  • Deaton R., Murphy R.C., Garzon M., Franceschetti D.R., Stevens, Jr. S.E.: Good encodings for DNA-based solutions to combinatorial problems. In: Proceedings of 2nd Annual Meeting DNA Based Computation, pp. 247–258 (1996)

  • Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  • Faulhammer, D., Cukras, A.R., Lipton, R.J., Landweber, L.F.: Molecular computation: RNA solutions to chess problems. Proc. Natl. Acad. Sci. USA 97, 1385–1389 (2000)

    Article  Google Scholar 

  • Feldkamp, U., Saghafi, S., Banzhaf, W., Rauhe, H.: DNA sequence generator—a program for the construction of DNA sequences. In: Proceedings of 7th International Workshop DNA Based Computers, pp. 179–188 (2001)

  • Fogel, G.B., Porto, V.W., Varga, G., Dow, E.R., Craven, A.M., Powers, D.M., Harlow, H.B., Su, E.W., Onyia, J.E., Su, C.: Evolutionary computation for discovery of composite transcription factor binding sites. Nucleic Acids Res. 36(21), e142 (2008)

    Article  Google Scholar 

  • Frutos, G., Thiel, A.J., Condon, A.E., Smith, L.M., Corn, R.M.: DNA computing at surfaces: four base mismatch word design. In: Proceedings of 3rd DIMACS Workshop DNA Based Computers, pp. 238 (1997)

  • Garzon, M.H., Deaton, R.J.: Biomolecular computing and programming. IEEE Trans. Evol. Comput. 3, 236–250 (1999)

    Article  Google Scholar 

  • Handl, J., Kell, D.B., Knowles, J.: Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans. Comp. Biol. Bioinf. 4(2), 279–292 (2007)

    Article  Google Scholar 

  • Hartemink, J., Gifford, D.K., Khodor, J.: Automated constraint based nucleotide sequence selection for DNA computation. In: Proceedings of the 4th DIMACS Workshop DNA Based Computers, pp. 227–235 (1998)

  • Heitsch, E., Condon, A.E., Hoos, H.H.: From RNA secondary structure to coding theory: a combinatorial approach. In: Proceedings of the 8th International Workshop DNA Based Computers, pp. 215–228 (2002)

  • Hongyan, Z., Xiyu, L.: Improved genetic algorithm for designing DNA sequences. In: Proceedings of the 2nd International Symposium on Electronic Commerce and Security, pp. 514–518 (2009)

  • Ibrahim, Z., Khalid, N.K., Buyamin, S., Ibrahim, I., Mukred, J.A.A., Yusof, Z.M., Mohamad, M.S., Mokhtar, N., Saaid, M.F.M., Engelbrecht, A.: DNA sequence design for DNA computation based on binary particle swarm optimization. Int. J Innovative Comput. Inf. Control 8(5B), 3441–3450 (2012)

    Google Scholar 

  • Khalid, N.K., Kurniawan, T.B., Ibrahim, Z., Yusof, Z.M., Khalid, M., Engelbrecht, A.P.: A model to optimize DNA sequences based on particle swarm optimization. In: Proceedings of the 2nd Asia International Conference on Modeling & Simulation, pp. 534–539 (2008)

  • Knowles, J., Corne, D.: The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimization. In: Proceedings of the Congress on Evolutionary Computation (CEC), pp. 98–105 (1999)

  • Kruskal, W.H., Wallis, W.A.: Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47(260), 583–621 (1952)

    Article  MATH  Google Scholar 

  • Kurniawan, T.B., Khalid, N.K., Ibrahim, Z., Khalid, M., Middendorf, M.: Evaluation of ordering methods for DNA sequence design based on ant colony system. In: Proceedings of the 2nd Asia International Conference on Modeling & Simulation, pp. 905–910 (2008)

  • Kurniawan, T.B., Khalid, N.K., Ibrahim, Z., Abidin, M.S.Z., Khalid, M.: Sequence design for direct-proportional length-based DNA computing using population-based ant colony optimization. In: Procedings of the ICROS-SICE International Joint Conference, pp. 1486–1491 (2009)

  • Lindman, H.R.: Analysis of Variance in Complex Experimental Designs. Erlbaum. Ed. Freeman & Co, New York (1974)

    MATH  Google Scholar 

  • Liu, W., Wang, S., Gao, L., Zhang, F., Xu, J.: DNA sequence design based on template strategy. J. Chem. Inf. Comput. Sci. 43, 2014–2018 (2003)

    Article  Google Scholar 

  • Marathe, A., Condon, A.E., Corn, R.M.: On combinatorial DNA word design. In: Proceedings of the 5th DIMACS Workshop DNA Based Computers, pp. 75–89 (1999)

  • Mustaza, S.M., Abidin, A.F.Z., Ibrahim, Z., Shamsudin, M.A., Husain, A.R., Mukred, J.A.A.: A modified computational model of ant colony system in DNA sequence design. In: Proceedings of the IEEE Student Conference on Research and Development (SCOReD), pp. 169–173 (2011)

  • Niknam, T., Azizipanah-Abarghooee, R., Narimani, M.R.: A new multiobjective approach based on TLBO for location of automatic voltage regulators in distribution systems. Eng. Appl. Artif. Intell. 25(8), 1577–1588 (2012a)

    Article  Google Scholar 

  • Niknam, T., Golestaneh, F., Sadeghi, M.S.: \(\uptheta \)—multiobjective teaching-learning-based optimization for dynamic economic emission dispatch. IEEE Syst. J. 6(2), 341–352 (2012b)

    Article  Google Scholar 

  • Penchovsky, R., Ackermann, J.: DNA library design for molecular computation. J. Comput. Biol. 10(2), 215–229 (2003)

    Article  Google Scholar 

  • Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf. Sci. 183(1), 1–15 (2012a)

    Article  MathSciNet  Google Scholar 

  • Rao, R.V., Patel, V.: Multi-objective optimization of combined Brayton and inverse Brayton cycle using advanced optimization algorithms. Eng. Optim. 44, 965–983 (2012b)

    Article  Google Scholar 

  • Rao, R.V., Kalyankar, V.D.: Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Eng. Appl. Artif. Intell. 26, 524–531 (2013a)

    Article  Google Scholar 

  • Rao, R.V., Patel, V.: Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Int. J. Ind. Eng. Comput. 4(1), 29–50 (2013b)

    Google Scholar 

  • Rao, R.V., Patel, V.: Multi-objective optimization of two stage thermoelectric cooler using a modified teaching-learning-based-optimization algorithm. Eng. Appl. Artif. Intell. 26, 430–445 (2013c)

    Article  Google Scholar 

  • Robič, T., Filipič, B.: DEMO: differential evolution for multiobjective optimization. In LNCS 3410, Evolutionary Multi-Criterion Optimization, pp. 520–533 (2005)

  • Ruben, J., Freeland, S.J., Landweber, L.: PUNCH: An evolutionary algorithm for optimizing bit set selection. In: Proceedings of the 7th International Workshop DNA Based Computers, pp. 260–270 (2001)

  • Santa Lucia, Jr. J.: A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. In: Proceedings of the National Academy Sciences of the United States of America vol. 95, pp. 1460–1465 (1998)

  • Sheskin, J.: Handbook of Parametric and Nonparametric Statistical Procedures, 5th edn. Chapman & Hall, Boca Raton (2011)

    MATH  Google Scholar 

  • Shin, S.H., Kim, D.M., Lee, I.H., Zhang, B.T.: Evolutionary sequence generation for reliable DNA computing. In: Proceedings of the Congress on Evolutionary Computation (CEC), pp. 79–84 (2002)

  • Shin, S.H., Lee, I.H., Kim, D.M., Zhang, B.T.: Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing. IEEE Trans. Evol. Comput. 9(2), 143–158 (2005)

    Article  Google Scholar 

  • Tanaka, F., Nakatsugawa, M., Yamamoto, M., Shiba, T., Ohuchi, A.: Developing support system for sequence design in DNA computing. In: Proceedings of the 7th International Workshop DNA Based Computers, pp. 340–349 (2001)

  • Tanaka, F., Nakatsugawa, M., Yamamoto, M., Shiba, T., Ohuchi, A.: Toward a general-purpose sequence design system in DNA computing. In: Proceedings of Congress on Evolutionary Computation (CEC), pp. 73–78 (2002)

  • Talbi, E.G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8, 541–564 (2002)

    Article  Google Scholar 

  • Wang, Y., Cai, Z., Guo, G., Zhou, Y.: Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Trans. Syst. Man Cybern. B 37(3), 560–575 (2007)

    Article  Google Scholar 

  • Wang, Y., Shen, Y.; Zhang, X., Cui, G.: DNA codewords design using the improved NSGA-II algorithms. In: Proceedings of the 4th International Conference on Bio-Inspired Computing, pp. 48–52 (2009)

  • Xiao, J., Cheng, Z.: DNA sequences optimization based on gravitational search algorithm for reliable DNA computing. In: Proceedings of the Sixth International Conference on Bio-Inspired Computing, pp. 103–107 (2011)

  • Xu, C., Zhang, Q., Wang, B., Zhang, R.: Research on the DNA sequence design based on GA/PSO algorithms. In: Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering, pp. 816–819 (2008)

  • Zhang, B.T., Shin, S.Y.: Molecular algorithms for efficient and reliable DNA computing. In: Proceeding of Genetic Program, pp. 735–742 (1998)

  • Zhang, Q., Wang, B., Wei, X., Fang, X., Zhou, C.: DNA word set design based on minimum free energy. IEEE Trans. Nanobiosci. 9(4), 273–277 (2010)

    Article  Google Scholar 

  • Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  • Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. In: Proceedings of EUROGEN’02, pp. 95–100 (2002)

  • Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

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Acknowledgments

This work was partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the contract TIN2012-30685 (BIO project).

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  1. Department of Computer Science, University of Extremadura, Escuela Politecnica, Campus Universitario s/n, 10003, Cáceres, Spain

    Jose M. Chaves-González

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Correspondence to Jose M. Chaves-González.

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Chaves-González, J.M. Hybrid multiobjective metaheuristics for the design of reliable DNA libraries. J Heuristics 21, 751–788 (2015). https://doi.org/10.1007/s10732-015-9298-x

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