Skip to main content
Log in

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

  • Published:
The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  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. G. Michal, Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology, John Wiley & Sons, 1998.

  3. STKE’s connection map database. http://stke.sciencemag.org/cm/, 2007.

  4. KEGG: Kyoto Encyclopedia of Genes and Genomes. http://www.genome.jp/kegg, 2007.

  5. Signaling Pathway Database. http://www.grt.kyushu-u.ac.jp/grt-docs/mogt/sub_study_contents_06.html, 2007.

  6. Metabolic Pathways of Biochemistry. http://www.gwu.edu/~mpb/, 1998.

  7. Encyclopedia of Escherichia coli K12 Genes and Metabolism. http://www.ecocyc.org/, 2007.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  12. J.F. Keane, C. Bradley and C. Ebeling, “A Compiled Accelerator for Biological Cell Signaling Simulations,” in Proc. FPGA2004, 2004, pp. 233–241.

  13. L. Salwinski and D. Eisenberg, “In Silico Simulation of Biological Network Dynamics,” Nat. Biotechnol., vol. 22, no. 8, 2004, pp. 1017–1019.

    Article  Google Scholar 

  14. M. Yoshimi, Y. Osana, T. Fukushima and H. Amano, “Stochastic Simulation for Biochemical Reactions on FPGA,” Proc. FPL2004, LNCS3203, 2004, pp. 105–114.

  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.

    Article  Google Scholar 

  16. K. Ritchie and A. Kusumi, “Single-particle Tracking Image Microscopy,” Methods Enzymol., vol. 360, 2003, pp. 618–634.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  23. D.P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics, Cambridge University Press, 2000.

  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. P.D. Coddington, “Random Number Generators for Parallel Computers”, in Proc. Supercomputing 1996, NHSE Review 1996(2), 1997.

  26. M. Barel, “Fast Hardware Random Number Generator for the Tausworthe Sequence,” in Proc. the 16th Annual Symposium on Simulation, 1983, pp. 121–135.

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yoshiki Yamaguchi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yamaguchi, Y., Maruyama, T., Azuma, R. et al. Mesoscopic-level Simulation of Dynamics and Interactions of Biological Molecules Using Monte Carlo Simulation. J VLSI Sign Process Syst Sign Im 48, 287–299 (2007). https://doi.org/10.1007/s11265-007-0072-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-007-0072-7

Keywords

Navigation