Name
Title | ||
|---|---|---|
A computational approach to increase time scales in Brownian dynamics-based reaction-diffusion modeling. |
Abstract | ||
|---|---|---|
Particle-based Brownian dynamics simulations offer the opportunity to not only simulate diffusion of particles but also the reactions between them. They therefore provide an opportunity to integrate varied biological data into spatially explicit models of biological processes, such as signal transduction or mitosis. However, particle based reaction-diffusion methods often are hampered by the relatively small time step needed for accurate description of the reaction-diffusion framework. Such small time steps often prevent simulation times that are relevant for biological processes. It is therefore of great importance to develop reaction-diffusion methods that tolerate larger time steps while maintaining relatively high accuracy. Here, we provide an algorithm, which detects potential particle collisions prior to a BD-based particle displacement and at the same time rigorously obeys the detailed balance rule of equilibrium reactions. We can show that for reaction-diffusion processes of particles mimicking proteins, the method can increase the typical BD time step by an order of magnitude while maintaining similar accuracy in the reaction diffusion modelling. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1089/cmb.2012.0027 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
Brownian dynamics,protein-protein interactions,reaction-diffusion | Biological data,Artificial intelligence,Molecular dynamics,Statistical physics,Detailed balance,Particle displacement,Simulation,Brownian dynamics,Reaction–diffusion system,Machine learning,Mathematics,Particle,Chemical equilibrium | Journal |
Volume | Issue | ISSN |
19.0 | 6 | 1066-5277 |
Citations | PageRank | References |
5 | 0.56 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zach Frazier | 1 | 29 | 1.39 |
| Frank Alber | 2 | 33 | 4.63 |