Cargando…

Particle simulation approach for subcellular dynamics and interactions of biological molecules

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....

Descripción completa

Detalles Bibliográficos
Autores principales: Azuma, Ryuzo, Kitagawa, Tetsuji, Kobayashi, Hiroshi, Konagaya, Akihiko
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1780110/
https://www.ncbi.nlm.nih.gov/pubmed/17217513
http://dx.doi.org/10.1186/1471-2105-7-S4-S20
Descripción
Sumario: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.