Advertisement

Digital Information Encoding on DNA

  • Max H. Garzon
  • Kiranchand V. Bobba
  • Bryan P. Hyde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2950)

Abstract

Novel approaches to information encoding with DNA are explored using a new Watson-Crick structure for binary strings more appropriate to model DNA hybridization. First, a Gibbs energy analysis of codeword sets is obtained by using a template and extant error-correcting codes. Template-based codes have too low Gibbs energies that allow cross-hybridization. Second, a new technique is presented to construct arbitrarily large sets of noncrosshybridizing codewords of high quality by two major criteria. They have a large minimum number of mismatches between arbitrary pairs of words and alignments; moreover, their pairwise Gibbs energies of hybridization remain bounded within a safe region according to a modified nearest-neighbor model that has been verified in vitro. The technique is scalable to long strands of up to 150-mers, is in principle implementable in vitro, and may be useful in further combinatorial analysis of DNA structures. Finally, a novel method to encode abiotic information in DNA arrays is defined and some preliminary experimental results are discussed. These new methods can be regarded as a different implementation of Tom Head’s idea of writing on DNA molecules [22], although only through hybridization.

Keywords

Gibbs Energy Strand Length Template Code Base Strand Biomolecular Computation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adleman, L.: Molecular computation of solutions of combinatorial problems. Science 266, 1021–1024 (1994)CrossRefGoogle Scholar
  2. 2.
    Arita, M., Kobayashi, S.: DNA Sequence Design Using Templates. New Generation Computing 20(3), 263–277 (2002); See also [20], pp. 205–214Google Scholar
  3. 3.
    Baum, E.: Building an Associative Memory Vastly larger than the Brain. Science 268, 583–585 (1995)CrossRefGoogle Scholar
  4. 4.
    Brenneman, B., Condon, A.: Sequence Design for Biomolecular Computation (2001) (in press), Available at http://www.cs.ubc.edu/~condon/papers/wordsurvey.ps
  5. 5.
    Bi, H., Chen, J., Deaton, R., Garzon, M., Rubin, H., Wood, D.: A PCR-based Protocol fo. In: Vitro Selection of Non-Crosshybridizing Oligonucleotides; In: [20], J. of Natural Computing, 196-204 (2003) (in press)Google Scholar
  6. 6.
    Condon, A., Rozenberg, G. (eds.): DNA 2000. LNCS, vol. 2054. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  7. 7.
    Deaton, R.J., Chen, J., Bi, H., Garzon, M., Rubin, H., Wood, D.H.: A PCR-based protocol for In Vitro Selection of Non-crosshybridizing Oligonucleotides. In: [20], pp. 105–114 (2002)Google Scholar
  8. 8.
    Deaton, R.J., Chen, J., Bi, H., Rose, J.A.: A Software Tool for Generating Non-crosshybridizing Libraries of DNA Oligonucleotides. In: [20], pp. 211–220 (2002b)Google Scholar
  9. 9.
    Deaton, R., Garzon, M., Murphy, R.E., Rose, J.A., Franceschetti, D.R., Stevens Jr., S.E.: The Reliability and Efficiency of a DNA Computation. Phys. Rev. Lett. 80, 417 (1998)CrossRefGoogle Scholar
  10. 10.
    Feldkamp, U., Ruhe, H.: Sofware Tools for DNA Sequence Design. J. Genetic Programming and Evolvable Machines 4, 153–171 (2003)CrossRefGoogle Scholar
  11. 11.
    Frutos, A.G., Condon, A., Corn, R.: Demonstration of a Word Design Strategy for DNA Computing on Surface. Nucleic Acids Research 25, 4748–4757 (1997)CrossRefGoogle Scholar
  12. 12.
    Garzon, M. (ed.): Biomolecular Machines and Artificial Evolution. Special Issue of the Journal of Genetic Programming and Evolvable Machines, vol. 4(2). Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  13. 13.
    Garzon, M., Blain, D., Bobba, K., Neel, A., West, M.: Self-Assembly of DNA-like structures In-Silico. In: [12], pp. 185–200 (2003)Google Scholar
  14. 14.
    Garzon, M., Neel, A., Bobba, K.: Efficiency and Reliability of Semantic Retrieval in DNA-based Memories. In: Chen, J., Reif, J.H. (eds.) DNA 2003. LNCS, vol. 2943, Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Garzon, M., Oehmen, C.: Biomolecular Computing in Virtual Test Tubes. In: [24], pp. 117–128 (2001b)Google Scholar
  16. 16.
    Garzon, M., Deaton, R.J.: Biomolecular Computing: a Definition. Kunstliche Intelligenz 1, 63–72 (2000)Google Scholar
  17. 17.
    Garzon, M., Deaton, R.J.: Biomolecular Computing and Programming. IEEE Trans. on Evolutionary Comp. 3(2), 36–50 (1999)Google Scholar
  18. 18.
    Garzon, M., Neathery, P.I., Deaton, R., Murphy, R.C., Franceschetti, D.R., Stevens Jr, S.E.: A New Metric for DNA Computing. In: [25], pp. 472–478 (1997)Google Scholar
  19. 19.
    Garzon, M., Deaton, R., Neathery, P., Murphy, R.C., Franceschetti, D.R., Stevens Jr, E.: On the Encoding Problem for DNA Computing. In: Poster at The Third DIMACS Workshop on DNA-based Computing, U of Pennsylvania. Preliminary Proceedings, pp. 230–237 (1997)Google Scholar
  20. 20.
    Hagiya, M., Ohuchi, A. (eds.): DNA 2002. LNCS, vol. 2568. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  21. 21.
    Head, T., Yamamura, M., Gal, S.: Relativized code concepts and multi-tube DNA dictionaries (2001) (in press)Google Scholar
  22. 22.
    Head, T., Yamamura, M., Gal, S.: Aqueous Computing: Writing on Molecules. In: Proceedings of the Congress on Evolutionary Computing, CEC 1999 (1999)Google Scholar
  23. 23.
    Head, T.: Formal Language Thery and DNA; An Analysis of the Generative Capacity of Specific Recombinant Behaviors. Bull. of Mathematical Biology 49(6), 737–759 (1986)MathSciNetGoogle Scholar
  24. 24.
    Hinze, T., Hatnik, U., Sturm, M.: An object oriented simulation of real occurring molecular biological processes for DNA computing and its experimental verification. In: Jonoska, N., Seeman, N.C. (eds.) DNA 2001. LNCS, vol. 2340, p. 1. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  25. 25.
    Koza, J.R., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M., Iba, H., Riolo, R.L. (eds.): Proc. 2nd Annual Genetic Programming Conference. Morgan Kaufmann, San Mateo (1997)Google Scholar
  26. 26.
    Roman, J.: The Theory of Error-Correcting Codes. Springer, Berlin (1995)Google Scholar
  27. 27.
    Rubin, H., Wood, D. (eds.): Third DIMACS Workshop on DNA-Based Computers, The University of Pennsylvania (1997); DIMACS Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, Providence, RI, vol. 48 (1999)Google Scholar
  28. 28.
    SantaLucia Jr., J., Allawi, H.T., Seneviratne, P.A.: Improved Nearest Neighbor Paramemeters for Predicting Duplex Stability. Biochemistry 35, 3555–3562 (1990)CrossRefGoogle Scholar
  29. 29.
    Shor, P.W.: Fault-Tolerant Quantum Computation. In: Proc. 37th FOCS, pp. 56–65 (1996)Google Scholar
  30. 30.
    Wetmur, J.G.: Physical Chemistry of Nucleic Acid Hybridization. In: [27], pp. 1–23 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Max H. Garzon
    • 1
  • Kiranchand V. Bobba
    • 1
  • Bryan P. Hyde
    • 2
  1. 1.Computer ScienceThe University of MemphisMemphisU.S.A.
  2. 2.SAIC-Scientific Applications International CorporationHuntsvilleU.S.A.

Personalised recommendations