Mesoscopic-level Simulation of Dynamics and Interactions of Biological Molecules Using Monte Carlo Simulation

  • Yoshiki Yamaguchi
  • Tsutomu Maruyama
  • Ryuzo Azuma
  • Moritoshi Yasunaga
  • Akihiko Konagaya
Article
  • 83 Downloads

Abstract

A mesoscopic-level method for clarifying living cell dynamics is described that uses Monte Carlo simulation of biological molecule interactions. The molecules are described as particles that take a random walk in 3-dimensional discrete space. Many kinds of molecules (including complex forms) are supported, so complex reactions with enzymes can be simulated. Also described is an field programmable gate array system with reconfigurable hardware that that will support complete modeling of an entire cell. Two-phase processing (migration and reaction) is used to simulate the complex reactions, so the method can be implemented in a limited amount of hardware. The migration and reaction circuits are deeply pipelined, resulting in high performance. Estimated performance is 30 times faster than with a 3.2-GHz Pentium 4 computer. This approach should make it possible to eventually simulate cell interactions involving one billion particles.

Keywords

Monte Carlo simulation signal transduction pathways FPGA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. Kanehisa, “Toward Pathway Engineering: A New Database of Genetic and Molecular Pathways,” Sci. Technol. Jpn., vol. 59, 1996, pp. 34–38.Google Scholar
  2. 2.
    G. Michal, Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology, John Wiley & Sons, 1998.Google Scholar
  3. 3.
    STKE’s connection map database. http://stke.sciencemag.org/cm/, 2007.
  4. 4.
    KEGG: Kyoto Encyclopedia of Genes and Genomes. http://www.genome.jp/kegg, 2007.
  5. 5.
  6. 6.
    Metabolic Pathways of Biochemistry. http://www.gwu.edu/~mpb/, 1998.
  7. 7.
    Encyclopedia of Escherichia coli K12 Genes and Metabolism. http://www.ecocyc.org/, 2007.
  8. 8.
    P. Mendes and D.B. Kell, “Non-linear Optimization of Biochemical Pathways: Applications to Metabolic Engineering and Parameter Estimation,” Bioinformatics, vol. 14, no. 10, 1998, pp. 869–883.CrossRefGoogle Scholar
  9. 9.
    I. Goryanin, T.C. Hodgman, and E. Selkov, “Mathematical Simulation and Analysis of Cellular Metabolism and Regulation,” Bioinformatics, vol. 15, no. 9, 1999, pp. 749–758.CrossRefGoogle Scholar
  10. 10.
    K. Takahashi, N. Ishikawa, Y. Sadamoto, H. Sasamoto, S. Ohta, A. Shiozawa, F. Miyoshi, Y. Naito, Y. Nakayama and M. Tomita, “E-Cell 2: Multi-platform E-cell Simulation System,” Bioinformatics, vol. 19, no. 13, 2003, 1727–1729.CrossRefGoogle Scholar
  11. 11.
    L.M. Loew and J.C. Schaff, “The Virtual Cell: A Software Environment for Computational Cell Biology,” Trends Biotechnol., vol. 19, no. 10, 2001, pp. 401–406.CrossRefGoogle Scholar
  12. 12.
    J.F. Keane, C. Bradley and C. Ebeling, “A Compiled Accelerator for Biological Cell Signaling Simulations,” in Proc. FPGA2004, 2004, pp. 233–241.Google Scholar
  13. 13.
    L. Salwinski and D. Eisenberg, “In Silico Simulation of Biological Network Dynamics,” Nat. Biotechnol., vol. 22, no. 8, 2004, pp. 1017–1019.CrossRefGoogle Scholar
  14. 14.
    M. Yoshimi, Y. Osana, T. Fukushima and H. Amano, “Stochastic Simulation for Biochemical Reactions on FPGA,” Proc. FPL2004, LNCS3203, 2004, pp. 105–114.Google Scholar
  15. 15.
    F. Daumas, N. Destainville, C. Millot, A. Lopez, D. Dean, and L. Salome, “Confined Diffusion without Fences of a G-protein-coupled Receptor as Revealed by Single Particle Tracking,” Biophys. J., vol. 84, no. 1, 2003, pp. 356–366.CrossRefGoogle Scholar
  16. 16.
    K. Ritchie and A. Kusumi, “Single-particle Tracking Image Microscopy,” Methods Enzymol., vol. 360, 2003, pp. 618–634.CrossRefGoogle Scholar
  17. 17.
    Y. Shav-Tal, X. Darzacq, S.M. Shenoy, D. Fusco, S.M. Janicki, D.L. Spector, and R.H. Singer, “Dynamics of Single mRNPs in Nuclei of Living Cells,” Science, vol. 304, no. 5678, 2004, pp. 1797–1800.CrossRefGoogle Scholar
  18. 18.
    D. Fusco, N. Accornero, B. Lavoie, S.M. Shenoy, J.M. Blanchard, R.H. Singer, and E. Bertrand, “Single mRNA Molecules Demonstrate Probabilistic Movement in Living Mammalian Cells,” Curr. Biol., vol. 13, no. 2, 2003, pp. 161–167.CrossRefGoogle Scholar
  19. 19.
    A. Suenaga, M. Hatakeyama, M. Ichikawa, X. Yu, N. Futatsugi, T. Narumi, K. Fukui, T. Terada, M. Taiji, M. Shirouzu, S. Yokoyama, and A. Konagaya, “Molecular Dynamics, Free Energy, and SPR Analyses of the Interactions between the SH2 Domain of Grb2 and ErbB Phosphotyrosyl Peptides,” Biochemistry, vol. 42, 2003, pp. 5195–5200.CrossRefGoogle Scholar
  20. 20.
    M. Taiji, T. Narumi, Y. Ohno, N. Futatsugi, A. Suenaga, N. Takada, and A. Konagaya, “Protein Explorer: A Petaflops Special-purpose Computer System for Molecular Dynamics Simulations,” in Proc. Supercomputing 2003, 2003, CD-ROM.Google Scholar
  21. 21.
    J.R. Weimar, “Cellular Automata Approaches to Enzymatic Reaction Networks,” in Proc. Fifth International Conference on Cellular Automata for Research and Industry, LNCS2493, 2002, pp. 294–303.Google Scholar
  22. 22.
    R. Azuma, K. Tetsuji, H. Kobayashi, and A. Konagaya, “Particle Simulation Approach for Subcellular Dynamics and Interactions of Biological Molecules,” BMC Bioinformatics, vol. 7, Suppl. 4, 2006, pp. S20–1–S20–13.Google Scholar
  23. 23.
    D.P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics, Cambridge University Press, 2000.Google Scholar
  24. 24.
    T.C. Meng, S. Somani, and P. Dhar, “Modeling and Simulation of Biological Systems with Stochasticity,” In Silico Biol., vol. 4, no. 3, 2004, pp. 293–309.Google Scholar
  25. 25.
    P.D. Coddington, “Random Number Generators for Parallel Computers”, in Proc. Supercomputing 1996, NHSE Review 1996(2), 1997.Google Scholar
  26. 26.
    M. Barel, “Fast Hardware Random Number Generator for the Tausworthe Sequence,” in Proc. the 16th Annual Symposium on Simulation, 1983, pp. 121–135.Google Scholar
  27. 27.
    C.-Y.F. Hung and J.E. Ferrell Jr. “Ultrasensitivity in the Mitogen-activated Protein Kinase Cascade,” Proc. Natl. Acad. Sci. U. S. A., vol. 93, 1996, pp. 10078–10083.CrossRefGoogle Scholar
  28. 28.
    Y. Yamaguchi, T. Maruyama, and T. Hoshino, “High Speed Hardware Computation of Co-evolution Models,” in Proc. European Conference on Artificial Life, LNCS1674, 1999, pp. 566–574.Google Scholar
  29. 29.
    B. Novak and J.J. Tyson, “Modeling the Cell Division Cycle: M-phase Trigger, Oscillations, and Size Control,” J. Theor. Biol., vol. 165, 1993, pp. 101–134.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Yoshiki Yamaguchi
    • 1
    • 2
  • Tsutomu Maruyama
    • 1
    • 2
  • Ryuzo Azuma
    • 2
  • Moritoshi Yasunaga
    • 1
  • Akihiko Konagaya
    • 2
    • 3
  1. 1.Graduate Schoo of Systems and Information Engineering, University of TsukubaTsukubaJapan
  2. 2.RIKEN Genomic Sciences CenterYokohamaJapan
  3. 3.Department of Mathematics and Computing SciencesTokyo Institute of TechnologyTokyoJapan

Personalised recommendations