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Abstract

A microtubule gliding assay is a biological experiment observing the dynamics of microtubules driven by motor proteins fixed on a glass surface. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics in silico, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation parameters in a real-time fashion. One of technical challenges when creating a real-time 3D simulation is balancing the 3D rendering and the computing performance. GPU programming plays an essential role in balancing the millions of tasks, and makes this real-time 3D simulation possible. By the use of GPGPU programming we are able to run the simulation in a massively parallel fashion, even when dealing with more complex interactions between microtubules such as overriding and snuggling.

Keywords

Microtubule Gliding Assay real-time 3D simulation CUDA DirectX 

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References

  1. 1.
    Murata, S., Konagaya, A., Kobayashi, S., Saito, H., Hagiya, M.: Molecular Robotics: A New Paradigm for Artifacts. New Generation Computing 31(1), 27–45 (2013)CrossRefGoogle Scholar
  2. 2.
    Howard, J., Hudspeth, A.J., Vale, R.D.: Movement of microtubules by single kinesin molecules. Nature 342, 154–158 (1989)CrossRefGoogle Scholar
  3. 3.
    Bohm, K.J., Stracke, R., Muhlig, P., Unger, E.: Nanotechnology 12, 238–244 (2001)CrossRefGoogle Scholar
  4. 4.
    Fischer, T., Agarwal, A., Hess, H.: A smart dust biosensor powered by kinesin motors. Nat. Nanotech. 4, 162–166 (2009)CrossRefGoogle Scholar
  5. 5.
    Akira, K., Jian, P.G., et al.: Formation of ring-shaped assembly of microtubules with a narrow size distribution at an air–buffer interface. Soft Matter 8, 10863–10867 (2012)CrossRefGoogle Scholar
  6. 6.
    Inoue, D., Kabir, A.M.R., Mayama, H., Gong, J.P., Sada, K., Kakugo, A.: Growth of ring-shaped microtubule assemblies through stepwise active self-organisation. Soft Matter 9, 7061–7068 (2013)CrossRefGoogle Scholar
  7. 7.
    Kraikivski, P., Lipowsky, R., Kierfeld, J.: Enhanced Ordering of Interacting Filaments by Molecular Motors, Phys. Rev. Lett. 96, 258103 (2009)CrossRefGoogle Scholar
  8. 8.
    Kong, K.Y., Marcus, A.I., Giannakakou, P., Alberti, C., Wang, M.D.: A Two Dimensional Simulation of Microtubule Dynamics. In: Proc. of the 5th Inter. Conf. on Information Technology and Application in Biomedicine, pp. 461–462 (2008)Google Scholar
  9. 9.
    Sherrod, A., Wendy, J.: Beginning DirectX 11 Game Programming, Boston: Course Technology PTR, Print (2011)Google Scholar
  10. 10.
    Luna, F.D., Dulles, V.A.: Introduction to 3D Game Programming with DirectX 11, Mercury Learning & Information (2012, Print)Google Scholar
  11. 11.
    Kabir, A.M.R., Inoue, D., Kakugo, A., Kamei, A., Gong, G.P.: Prolongation of the Active Lifetime of a Biomolecular Motor for in Vitro Motility Assay by Using an Inert Atmosphere. Langmuir 27(22), 13659–13668 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Greg Gutmann
    • 1
  • Daisuke Inoue
    • 2
  • Akira Kakugo
    • 2
    • 3
  • Akihiko Konagaya
    • 1
  1. 1.Department of Computational Intelligence and Systems ScienceTokyo Institute of TechnologyYokohamaJapan
  2. 2.Faculty of ScienceHokkaido UniversitySapporoJapan
  3. 3.Graduate School of Chemical Sciences and EngineeringHokkaido UniversitySapporoJapan

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