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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Open Access Research

Particle simulation approach for subcellular dynamics and interactions of biological molecules

Ryuzo Azuma1*, Tetsuji Kitagawa2, Hiroshi Kobayashi3 and Akihiko Konagaya12

Author Affiliations

1 RIKEN Genomic Sciences Center, 1-7-22 Suehiro Tsurumi, Yokohama, Kanagawa, 230-0045, Japan

2 Dept. of Mathematics and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo, 152-8552, Japan

3 Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo, Chiba, 260-8675, Japan

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BMC Bioinformatics 2006, 7(Suppl 4):S20  doi:10.1186/1471-2105-7-S4-S20

Published: 12 December 2006

Abstract

Background

Spatio-temporal dynamics within cells can now be visualized at appropriate resolution, due to the advances in molecular imaging technologies. Even single-particle tracking (SPT) and single fluorophore video imaging (SFVI) are now being applied to observation of molecular-level dynamics. However, little is known concerning how molecular-level dynamics affect properties at the cellular level.

Results

We propose an algorithm designed for three-dimensional simulation of the reaction-diffusion dynamics of molecules, based on a particle model. Chemical reactions proceed through the interactions of particles in space, with activation energies determining the rates of these chemical reactions at each interaction. This energy-based model can include the cellular membrane, membranes of other organelles, and cytoskeleton. The simulation algorithm was tested for a reversible enzyme reaction model and its validity was confirmed. Snapshot images taken from simulated molecular interactions on the cell-surface revealed clustering domains (size ~0.2 μm) associated with rafts. Sample trajectories of raft constructs exhibited "hop diffusion". These domains corralled the diffusive motion of membrane proteins.

Conclusion

These findings demonstrate that our approach is promising for modelling the localization properties of biological phenomena.