A Probabilistic Model of the DNA Conformational Change

  • Masashi Shiozaki
  • Hirotaka Ono
  • Kunihiko Sadakane
  • Masafumi Yamashita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4287)


Predicting the behavior of DNA molecules in vitro is one of the most fundamental issues on DNA computing, but is also known to be quite difficult. Shiozaki et al. proposed a probabilistic model that can simulate many features of biochemical experiments in terms of the reaction rate [7], although there are several differences between the biochemical experiments and the computational simulations on the model.

In this paper, we extend the model to support base pairs construction among k DNA sequences, which plays an essential role in realizing branch migrations. The simulation results have much more similarities to the biochemical experiments results than ones on the previous model, which implies that the analysis of the model may give some insight about the reaction rate. Through the analysis, we conclude this paper by giving a guideline for designing DNA sequences that can quickly react.


Computational Simulation Minimum Free Energy State Transition Probability Biochemical Experiment Branch Migration 
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.


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  1. 1.
    Flamm, C.: Kinetic Folding of RNA, Dissertation (1998)Google Scholar
  2. 2.
    Flamm, C., Fontana, W., Hofacker, I.L., Schuster, P.: RNA Folding at Elementary Step Resolution, Santa Fe Institute 12-078 (1999)Google Scholar
  3. 3.
    Hofacker, I.L.: The Vienna RNA Secondary Structure Server. Nucleic. Acids Res. 31, 3429–3431 (2003)CrossRefGoogle Scholar
  4. 4.
    Hofacker, I.L., Fontana, W., Stadler, P.F., Bonhoeffer, L.S., Tacker, M., Schuster, P.: Fast Folding and comparison of RNA Secondary Structures. Monatsh. Chem. 125, 167–188 (2003)CrossRefGoogle Scholar
  5. 5.
    Hartemink, A.J., Fifford, D.K.: Thermodynamic Simulation of Deoxyligonucleotide Hybridization for DNA Computation. DIMACS Series in Discrete Mathematics and Theoretical Computer ScienceGoogle Scholar
  6. 6.
    Kobayashi, S.: Strand Design for Molecular Computation, Sciences (2003)Google Scholar
  7. 7.
    Shiozaki, M., Ono, H., Sadakane, K., Yamashita, M.: Modeling DNA Conformation Change and Theoretical Analysis on the Reaction Rate. In: Preproceedings of the 11th International Meeting on DNA Computing (DNA11), vol. 408 (2005)Google Scholar
  8. 8.
    Tacker, M., Fontana, W., Stadler, P.F., Schuster, P.: Statistics of RNA Melting Kinetics. Eur. Biophys. J. 23, 29–38 (1993)Google Scholar
  9. 9.
    Uejima, H., Hagiya, M.: Analyzing Secondary Structure Transition Paths of DNA/RNA molecules. In: PreProceedings of the 9th International Meeting on DNA Computing (DNA9), pp. 4731–4741 (2004)Google Scholar
  10. 10.
    Wolfinger, M.T., Svrcek-Seiler, W.A., Flamm, C., Hofacker, I.L., Stadler, P.F.: Efficient Computation of RNA Folding Dynamics. J. Phys. A: Math. Gen. 37, 4731–4741 (2004)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Zucker, M.: The Equilibrium Partition Function (2003),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Masashi Shiozaki
    • 1
  • Hirotaka Ono
    • 1
  • Kunihiko Sadakane
    • 1
  • Masafumi Yamashita
    • 1
  1. 1.Dept. of Computer Science and Communication EngineeringKyushu University 

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