Real-time 3D microtubule gliding simulation accelerated by GPU computing

  • Gregory Gutmann
  • Daisuke Inoue
  • Akira Kakugo
  • Akihiko KonagayaEmail author
Research Article Special Issue on Intelligent Computing and Modeling in Lift System and Sustainable Environment


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 on computers, 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 the technical challenges when creating a real-time 3D simulation is balancing the 3D rendering and the computing performance. Graphics processor unit (GPU) programming plays an essential role in balancing the millions of tasks, and makes this real-time 3D simulation possible. By the use of general-purpose computing on graphics processing units (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. Due to performance being an important factor, a performance model has also been constructed from the analysis of the microtubule simulation and it is consistent with the performance measurements on different GPGPU architectures with regards to the number of cores and clock cycles.


Microtubule gliding assay 3D computer graphics and simulation parallel computing performance analysis generalpurpose computing on graphics processing units (GPGPU) compute unified device arshitecture (CUDA) DirectX 


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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Gregory Gutmann
    • 1
  • Daisuke Inoue
    • 2
  • Akira Kakugo
    • 2
    • 3
  • Akihiko Konagaya
    • 1
    Email author
  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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