The two-regime method for optimizing stochastic reaction-diffusion simulations

被引:75
作者
Flegg, Mark B. [1 ]
Chapman, S. Jonathan [1 ]
Erban, Radek [1 ]
机构
[1] Univ Oxford, Inst Math, Oxford OX1 3LB, England
基金
欧洲研究理事会;
关键词
stochastic modelling; reaction-diffusion processes; multi-scale simulation; hybrid algorithm; WHOLE-CELL SIMULATION; CHEMICAL-REACTIONS; DYNAMICS; KINETICS;
D O I
10.1098/rsif.2011.0574
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Spatial organization and noise play an important role in molecular systems biology. In recent years, a number of software packages have been developed for stochastic spatio-temporal simulation, ranging from detailed molecular-based approaches to less detailed compartment-based simulations. Compartment-based approaches yield quick and accurate mesoscopic results, but lack the level of detail that is characteristic of the computationally intensive molecular-based models. Often microscopic detail is only required in a small region (e.g. close to the cell membrane). Currently, the best way to achieve microscopic detail is to use a resource-intensive simulation over the whole domain. We develop the two-regime method (TRM) in which a molecular-based algorithm is used where desired and a compartment-based approach is used elsewhere. We present easy-to-implement coupling conditions which ensure that the TRM results have the same accuracy as a detailed molecular-based model in the whole simulation domain. Therefore, the TRM combines strengths of previously developed stochastic reaction-diffusion software to efficiently explore the behaviour of biological models. Illustrative examples and the mathematical justification of the TRM are also presented.
引用
收藏
页码:859 / 868
页数:10
相关论文
共 29 条
[1]  
Ander M., 2004, Systems Biology, V1, P129, DOI 10.1049/sb:20045017
[2]   Stochastic simulation of chemical reactions with spatial resolution and single molecule detail [J].
Andrews, SS ;
Bray, D .
PHYSICAL BIOLOGY, 2004, 1 (03) :137-151
[3]   Detailed Simulations of Cell Biology with Smoldyn 2.1 [J].
Andrews, Steven S. ;
Addy, Nathan J. ;
Brent, Roger ;
Arkin, Adam P. .
PLOS COMPUTATIONAL BIOLOGY, 2010, 6 (03)
[4]   Accurate particle-based simulation of adsorption, desorption and partial transmission [J].
Andrews, Steven S. .
PHYSICAL BIOLOGY, 2009, 6 (04)
[5]  
Bergmann S., 2007, PLOS COMPUT BIOL, V5, P0232
[6]  
Crank J., 1979, MATH DIFFUSION, V2nd
[7]   SIMULATION OF STOCHASTIC REACTION-DIFFUSION PROCESSES ON UNSTRUCTURED MESHES [J].
Engblom, Stefan ;
Ferm, Lars ;
Hellander, Andreas ;
Lotstedt, Per .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2009, 31 (03) :1774-1797
[8]  
Erban R., 2007, A practical guide to stochastic simulations of reaction-diffusion processes
[9]   Reactive boundary conditions for stochastic simulations of reaction-diffusion processes [J].
Erban, Radek ;
Chapman, S. Jonathan .
PHYSICAL BIOLOGY, 2007, 4 (01) :16-28
[10]   Stochastic modelling of reaction-diffusion processes: algorithms for bimolecular reactions [J].
Erban, Radek ;
Chapman, S. Jonathan .
PHYSICAL BIOLOGY, 2009, 6 (04)