Study of the influence of ester orientation on the thermal stability of the smectic C phase: Simulation investigation
Two series of mesogenic molecules have been simulated using molecular dynamics with a full atomistic representation. The two series differ from each other only with the orientation of an ester linker ...
Two series of mesogenic molecules have been simulated using molecular dynamics with a full atomistic representation. The two series differ from each other only with the orientation of an ester linker ...
Conformational response of supercoiled DNA to confinement in a nanochannel
Monte Carlo simulations were done to study the conformation of supercoiled DNA confined in a nanochannel. The molecule has a superhelical density of around −0.05 and is bathed in a monovalent sa...
Monte Carlo simulations were done to study the conformation of supercoiled DNA confined in a nanochannel. The molecule has a superhelical density of around −0.05 and is bathed in a monovalent sa...
An efficient and exact stochastic simulation method to analyze rare events in biochemical systems
J. Chem. Phys. 129, 165101 (2008); doi:10.1063/1.2987701
Published 22 October 2008
You are not logged in to this journal. Log in
In robust biological systems, wide deviations from highly controlled normal behavior may be rare, yet they may result in catastrophic complications. While in silico analysis has gained an appreciation as a tool to offer insights into system-level properties of biological systems, analysis of such rare events provides a particularly challenging computational problem. This paper proposes an efficient stochastic simulation method to analyze rare events in biochemical systems. Our new approach can substantially increase the frequency of the rare events of interest by appropriately manipulating the underlying probability measure of the system, allowing high-precision results to be obtained with substantially fewer simulation runs than the conventional direct Monte Carlo simulation. Here, we show the algorithm of our new approach, and we apply it to the analysis of rare deviant transitions of two systems, resulting in several orders of magnitude speedup in generating high-precision estimates compared with the conventional Monte Carlo simulation.
©2008 American Institute of Physics
| History: | Received 7 July 2008; accepted 28 August 2008; published 22 October 2008 |
| Permalink: |
http://link.aip.org/link/?JCPSA6/129/165101/1 |
| BUY THIS ARTICLE | (US$28) |
|
|
|
|
Connotea
CiteULike
del.icio.us
BibSonomy
KEYWORDS and PACS
biochemistry,
biology computing,
chemistry computing,
molecular biophysics,
reaction kinetics theory,
stochastic processes
- 87.15.A-
Theory, modeling and computer simulation in molecular biophysics - 87.15.R-
Biochemical reactions and kinetics - 82.39.-k
Chemical kinetics in biological systems - 82.20.Wt
Computational modeling and simulation of chemical kinetics - 82.20.Uv
Stochastic theories of rate constants in chemical kinetics - YEAR: 2008
RELATED DATABASES
PUBLICATION DATA
0021-9606 (print)
1089-7690 (online)
AIP
REFERENCES (26)
For access to fully linked references, you need to log in.
For access to fully linked references, you need to Log in.
- M. Csete and J. Doyle,
Trends Biotechnol. 22, 446 (2004) . - G. Egger, G. Liang, A. Aparicio, and P. A. Jones,
Nature (London) 429, 457 (2004) . - M. Esteller,
N. Engl. J. Med. 358, 1148 (2008) . - A. P. Feinberg,
Nature (London) 447, 433 (2007) . - Y. Jiang, J. Bressler, and A. L. Beaudet,
Annual Review of Genomics and Human Genetics 5, 479 (2004) . - J. M. Melief,
Nature 450, 803 (2007) . - D. T. Gillespie, Handbook of Materials Modeling (Springer, New York, 2005), Chap. 5.11, pp. 1735–1752.
- D. T. Gillespie,
Annu. Rev. Phys. Chem. 58, 35 (2007) . - M. S. Samoilov and A. P. Arkin,
Nat. Biotechnol. 24, 1235 (2006) . - D. T. Gillespie,
Physica A 188, 404 (1992) . - D. A. McQuarrie,
J. Appl. Probab. 4, 413 (1967) . - D. T. Gillespie,
J. Comput. Phys. 22, 403 (1976) . - D. T. Gillespie,
J. Phys. Chem. 81, 2340 (1977) . - M. Ptashne, A Genetic Switch (Cell Press & Blackwell Scientific, Cambridge, MA, 1992).
- J. W. Little, D. P. Shepley, and D. W. Wert,
EMBO J. 18, 4299 (1999) . - P. W. Glynn and D. L. Iglehart, Manage. Sci. 35, 1367 (1989).
- N. G. van Kampen, Stochastic Processes in Physics and Chemistry (Elsevier, Amsterdam, 1992).
- C. W. Gardiner, Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences, 3rd ed. (Springer, New York, 2004).
- H. Kuwahara, C. Myers, M. Samoilov, N. Barker, and A. Arkin, in Transactions on Computational Systems Biology VI, Lecture Notes in Computer Science (Springer, Berlin, 2006), pp. 150–175.
- B. Munsky and M. Khammash, J. Chem. Phys. 124, 044104 (2006).
- R. L. Graham, D. E. Knuth, and O. Patashnik, Concrete Mathematics: A Foundation for Computer Science, (Addison-Wesley, Boston, MA, 1994).
- Y. Cao, H. Li, and L. Petzold, J. Chem. Phys. 121, 4059 (2004).
- J. M. McCollum, G. D. Peterson, C. D. Cox, M. L. Simpson, and N. F. Samatova,
Comput. Biol. Chem. 30, 39 (2006) . - M. Samoilov, S. Plyasunov, and A. P. Arkin,
Proc. Natl. Acad. Sci. U.S.A. 102, 2310 (2005) . - D. Gross and D. R. Miller,
Oper. Res. 32, 343 (1984) . - P. D. Welch, in The Computer Performance Modeling Handbook, edited by S. Lavenberg (Academic, New York, 1983), pp. 268–328.
