MesoRD 1.0: Stochastic reaction-diffusion simulations in the microscopic limit

Bioinformatics

Volumn 28, Issue 23, 2012, Pages 3155-3157

  Fange, David ^a   Mahmutovic, Anel ^a   Elf, Johan ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; CHEMICAL MODEL; COMPUTER PROGRAM; COMPUTER SIMULATION; DIFFUSION; STATISTICS; SYSTEMS BIOLOGY;

ALGORITHMS; COMPUTER SIMULATION; DIFFUSION; MODELS, CHEMICAL; SOFTWARE; STOCHASTIC PROCESSES; SYSTEMS BIOLOGY;

EID: 84870396583     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts584     Document Type: Article

References (22)

1. Ander, M. et al. (2004) SmartCell, a framework to simulate cellular processes that combines stochasticapproximation with diffusion and localisation: analysis of simple networks. Syst. Biol., 1, 129-138.
2. Berg, O.G. (1978) Model for statistical fluctuations of protein numbers in a micro-bial population. J. Theor. Biol., 71, 587-603.
3. Boulianne, L. et al. (2008) GridCell: a stochastic particle-based biological system simulator. BMC Syst Biol., 2, 66.
4. Burrage, K. et al. (2011) Stochastic simulation for spatial modelling of dynamic processes in a living cell. In Koeppl, H. et al. (ed.) Design and Analysis of Biomolecular Circuits: Engineering: Approaches to Systems and Synthetic Biology. Springer, New York.
5. Collins, F.C. and Kimball, G.E. (1949) Diffusion-controlled reaction rates. J. Coll. Sci., 4, 425-437.
6. Drawert, B. et al. (2012) URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries. BMC Syst Biol., 6, 76.
7. Elf, J. and Ehrenberg, M. (2004) Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases. Syst Biol., 1, 230-236.
8. Erban, R. and Chapman, S.J. (2009) Stochastic modelling of reaction-diffusion processes: algorithms for bimolecular reactions. Phys. Biol., 6.
9. Fange, D. et al. (2010) Stochastic reaction-diffusion kinetics in the microscopic limit. Proc. Natl Acad. Sci. USA, 107, 19820-19825.
10. Fange, D. and Elf, J. (2006) Noise-induced Min phenotypes in E coli. Plos. Comput. Biol., 2, 637-648.
11. Finney, A. and Hucka, M. (2003) Systems biology markup language: level 2 and beyond. Biochem. Soc. T, 31, 1472-1473.
12. Gardiner, C.W. (2004) Handbook of Stochastic Methods for Physics, Chemistry, and the Natural Sciences. Springer series in synergetics. Springer, Berlin/New York.
13. Hattne, J. et al. (2005) Stochastic reaction-diffusion simulation with MesoRD. Bioinformatics, 21, 2923-2924.
14. Isaacson, S.A. (2008) Relationship between the reaction-diffusion master equation and particle tracking models. J. Phys. A, 41, 065003.
15. Kampen, N.G.V. (2007) Stochastic Processes in Physics and Chemistry. North-Holland personal library, Elsevier, Amsterdam/Boston.
16. Kerr, R.A. et al. (2008) Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J. Sci. Comput., 30, 3126.
17. Plimpton, S.J. and Slepoy, A. (2005) Microbial cell modeling via reacting diffusive particles. J. Phys. Conf. Ser., 16, 305-309.
18. Sanford, C. et al. (2006) Cell++-simulating biochemical pathways. Bioinformatics, 22, 2918-2925.
19. Shannon, C.E. (1948) A mathematical theory of communication. At&T Tech, J, 27, 379-423.
20. van Zon, J.S. and ten Wolde, P.R. (2005) Green's-function reaction dynamics: a particle-based approach for simulating biochemical networks in time and space. J. Chem. Phys., 123.
21. von Smoluchowski, M. (1917) Versuch einer mathematischen Theorie der Koagulationskinetik kolloider Losungen. Z. Phys. Chem., 92, 129-168.
22. Wils, S. and De Schutter, E. (2009) STEPS: modeling and simulating complex reaction-diffusion systems with python. Front. Neuroinform., 3, 15.