A distributed-based stochastic simulation algorithm for large biochemical reaction networks

2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

Volumn , Issue , 2007, Pages 502-505

  Niu, Jun Qing ^a   Zheng, Hao Ran ^a   Chen, Jiu Sheng ^a   Ma, Meng ^a   Wang, Xu Fa ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Component; Distributed based simulation; Multi agents; Stochastic simulation algorithm; Systems biology

Indexed keywords

ALGORITHMS; DYNAMIC PROGRAMMING; MATHEMATICAL MODELS; RANDOM PROCESSES; REACTION KINETICS;

DISTRIBUTED-BASED SIMULATION; STOCHASTIC SIMULATION ALGORITHMS; SYSTEMS BIOLOGY;

BIOCHEMISTRY;

EID: 35348883687     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICBBE.2007.132     Document Type: Conference Paper

References (19)

1. Kitano, H., Foundations of Systems Biology. 2001: The MIT Press.
2. Kitano, H., Systems biology: A brief overview. Science, 2002. 295(5560): p. 1662-1664.
3. Hasty, J., et al., Noise-based switches and amplifiers for gene expression. Proceedings of the National Academy of Sciences, 2000. 97(5): p. 2075-2080.
4. Keane, J.F., C. Bradley, and C. Ebeling. A compiled accelerator for biological cell signaling simulations. in ACM/SIGDA International Symposium on Field Programmable Gate Arrays - FPGA. 2004. Computer Science and Engineering, University of Washington, Seattle, WA, United States.
5. Paulsson, J., Summing up the noise in gene networks. Nature, 2004. 427: p. 415-418.
6. Gillespie, D.T., A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. The Journal of Physical Chemistry, 1976(22): p. 403-434.
7. Gillespie, D.T., Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry, 1977. 81(25): p. 2340-2361.
8. Gillespie, D.T., A rigorous derivation of the chemical master equation, in Physica A Statistical Mechanics and its Applications. 1992. p. 404-425.
9. Cao, Y., H. Li, and L. Petzold, Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. J Chem Phys, 2004. 121(9): p. 4059-4067.
10. Gillespie, D.T., Approximate accelerated stochastic simulation of chemically reacting systems. The Journal of Physical Chemistry, 2001. 115: p. 1716-1733.
11. Gibson, M. and J. Bruck, Efficient exact stochastic simulation of chemical systems with many species and many channels. The Journal of Physical Chemistry, 2000(A 104): p. 1876-1889.
12. McCollum, J.M., et al., The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior. Comput Biol Chem, 2005.
13. Lok, L., The need for speed in stochastic simulation. Nat Biotechnol, 2004. 22(8): p. 964-965.
14. Salwinski, L. and D. Eisenberg, In silico simulation of biological network dynamics. Nat Biotechnol, 2004. 22(8): p. 1017-1019.
15. Ridwan, A.M., A. Krishnan, and P. Dhar. A Parallel Implementation of Gillespie's Direct Method. in Computational Science - ICCS 2004, 4th International Conference,Kraków, Poland, June 6-9, 2004, Proceedings, Part II. 2004: Springer.
16. Arjunan, S., K. Takahashi, and M. Tomita, Shared-memory multiprocessing of Gillespie's stochastic simulation algorithm on E-Cell3. 2003, 4th International Conference on Systems Biology in St.Louis.
17. Dhar, P., et al., Cellware - A multi-algorithmic software for computational systems biology. Bioinformatics, 2004. 20(8): p. 1319-1321.
18. Dhar, P.K., et al., Grid cellware: the first grid-enabled tool for modeling and simulating cellular processes. Bioinformatics, 2005. 21(7): p. 1284-1287.
19. Levchenko, A., J. Bruck, and P.W. Sternberg, Scaffold proteins may biphasically affect the levels of mitogen-activated protein kinase signaling and reduce its threshold properties. PNAS, 2000. 97(11): p. 5818-5823.