New Generation Computing

, Volume 20, Issue 3, pp 307–315 | Cite as

Experimental efficiency of programmed mutagenesis

Special Issue
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Abstract

Mismatched DNA annealing followed by strand replication can cause the programmed evolution of DNA sequences. We have reported that this process is theoretically equivalent in computational power to a desktop computer by demonstrating a constructive way to encode arbitrary computations as DNA molecules within the framework of programmed mutagenesis, a system that consists solely of cycles of DNA annealing, polymerization, and ligation.1,2) Thus, programmed mutagenesis is theoretically universal and we report here the experimental efficiency of its primitive operations. The measured efficiency of an in vitro programmed mutagenesis system suggests that segregating the products of DNA replication into separate compartments would be an efficient way to implement molecular computation. For computer science, using single DNA molecules to represent the state of a computation holds the promise of a new paradigm of composable molecular computing. For biology, the demonstration that DNA sequences could guide their own evolution under computational rules may have implications as we begin to unravel the mysteries of genome encoding and natural evolution.

Keywords

Programmed Mutagenesis Universal System Biological Computing String Rewrite Systems Turing Machines 

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References

  1. 1).
    Khodor, J. and Gifford, D. K., “Programmed Mutagenesis is a Universal Model of Computation,” to appear in a special issue ofLecture Notes in Computer Science, Springer-Verlag, 2002.CrossRefGoogle Scholar
  2. 2).
    Khodor, J. and Gifford, D. K., “Programmed Mutagenesis is Universal,” to appear in a special issue ofTheory of Computing Systems, Springer-Verlag, 2002.MathSciNetCrossRefGoogle Scholar
  3. 3).
    Adleman, L., “Molecular Computation of Solutions to Combinatorial Problems,”Science, 266, 5187, pp. 1021–1024, 1994.CrossRefGoogle Scholar
  4. 4).
    Lipton, R. J., “DNA Solution to Computational Problems,”Science, 268, 5210, pp. 542–545, 1995.CrossRefGoogle Scholar
  5. 5).
    Boneh, D., Dunworth, C., Lipton, R. J., and Sgall, S., “On the Computational Power of DNA,”Discrete Applied Mathematics, 71, 1–3, pp. 79–94, 1996.MathSciNetCrossRefGoogle Scholar
  6. 6).
    Komiya, K., Sakamoto, K., Gouzu, H., Yokoyama, S., Arita, M., Nishilkawa, A., and Hagiya, M., “Successive State Transitions with I/O Interface by Molecules,”in Proc. of Sixth International Meeting on DNA Based Computers, Preliminary, pp. 21–30, 2000.Google Scholar
  7. 7).
    Sakamoto, K. Gouzu, H., Komiya, K., Kiga, D., Yokoyama, S., Yokomori, T., and Hagiya, M., “Molecular Computation by DNA Hairpin Formation,”Science, 288, 5469, pp. 1223–1226, 2000.CrossRefGoogle Scholar
  8. 8).
    Kari, L. and Thierrin, G., “Contextual Insertions/Deletions and Computability,”Information and Computation, 131, pp. 47–61, 1996.MathSciNetCrossRefGoogle Scholar
  9. 9).
    Landwebber, L. F. and Kari, L., “The Evolution of Cellular Computing: Nature's Solution to a Computational Problem,”Biosystems, 52, pp. 3–13, 1999.CrossRefGoogle Scholar
  10. 10).
    Beaver, D., “Molecular Computing,”1st DIMACS Workshop on DNA-based computers, Princeton, DIMACS Series, 27, pp. 29–36, 1996.Google Scholar
  11. 11).
    Head, T., “Formal Language Theory and DNA: an Analysis of the Generative Capacity of Specific Recombinant Behaviors,”Bulletin of Mathematical Biology, 49, pp. 737–759, 1987.MathSciNetCrossRefGoogle Scholar
  12. 12).
    Paun, G., “Computing with Membranes,”J Comput Syst Sci, 61, pp. 108–143, 2000.MathSciNetCrossRefGoogle Scholar
  13. 13).
    Winfree, E., “On the Computational Power of DNA Annealing and Ligation,”DNA Based Computers II: DIMACS Workshop, June 10–12, 1996, American Mathematical Society, pp. 191–213, 1998.Google Scholar
  14. 14).
    Mao, C., LaBean, T. H., Reif, J. H. and Seeman, N. C., “Logical Computation Using Algorithmic Self-assembly of DNA Triple-crossover Molecules,”Nature, 407, pp. 493–496, 2000.CrossRefGoogle Scholar
  15. 15).
    Hartemink, A. J., Gifford, D. K. and Khodor, J., “Automated Constraint-Governed Nucleotide Sequence Selection for DNA Computation,”Biosystems, 52, pp. 93–97, 1999.CrossRefGoogle Scholar
  16. 16).
    Khodor, J. and Gifford, D.K., “Design and Implementation of Computational Systems Based on Programmed Mutagenesis,”Biosystems, 52, pp. 227–235, 1999.CrossRefGoogle Scholar
  17. 17).
    Current Protocols in Molecular Biology (Ausubel, I. and Frederick, M., eds), 8.5, John Wiley & Sons, Inc, 1997.Google Scholar

Copyright information

© Ohmsha, Ltd. and Springer 2002

Authors and Affiliations

  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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