A pathwise derivative approach to the computation of parameter sensitivities in discrete stochastic chemical systems

Journal of Chemical Physics

Volumn 136, Issue 3, 2012, Pages

  Sheppard, Patrick W ^a   Rathinam, Muruhan ^b   Khammash, Mustafa ^a,c  

^a University of California   (United States)

^b UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

^c ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL REACTION NETWORKS; CHEMICAL SYSTEMS; COMPUTATIONAL COSTS; DERIVATIVE METHOD; FUNCTIONALS; INFINITESIMAL PERTURBATION ANALYSIS; LIKELIHOOD RATIOS; NUMERICAL ALGORITHMS; NUMERICAL EXAMPLE; ORDERS OF MAGNITUDE; PARAMETER SENSITIVITIES; PROBABILISTIC DESCRIPTIONS; REACTION PATHS; SENSITIVITY METHODS; SMALL PERTURBATIONS; STOCHASTIC SIMULATIONS; TIME CHANGE; VARIANCE REDUCTIONS;

ALGORITHMS; ESTIMATION; FINITE DIFFERENCE METHOD; MARKOV PROCESSES; MONTE CARLO METHODS; NUMERICAL METHODS; SENSITIVITY ANALYSIS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

CHEMICAL ANALYSIS;

ALGORITHM; ARTICLE; COMPUTER SIMULATION; KINETICS; MONTE CARLO METHOD; STATISTICS;

ALGORITHMS; COMPUTER SIMULATION; KINETICS; MONTE CARLO METHOD; STOCHASTIC PROCESSES;

EID: 84856484015     PISSN: 00219606     EISSN: None     Source Type: Journal    
DOI: 10.1063/1.3677230     Document Type: Article

References (18)

1. D. Gillespie, J. Phys. Chem. 81, 2340 (1977). 10.1021/j100540a008
2. B. Munsky and M. Khammash, J. Chem. Phys. 124, 044104 (2006). 10.1063/1.2145882 (Pubitemid 43166302)
3. R. Gunawan, Y. Cao, L. Petzold, and F. Doyle, Biophys. J. 88, 2530 (2005). 10.1529/biophysj.104.053405 (Pubitemid 40976122)
4. M. Rathinam, P. Sheppard, and M. Khammash, J. Chem. Phys. 132, 034103 (2010). 10.1063/1.3280166
5. S. Dandach and M. Khammash, PLOS Comput. Biol. 6 (2010). 10.1371/journal.pcbi.1000985
6. S. Plyasunov and A. Arkin, J. Comput. Phys. 221, 724 (2007). 10.1016/j.jcp.2006.06.047
7. Y. Ho and X. Cao, J. Optim. Theory Appl. 40, 559 (1983). 10.1007/BF00933971
8. P. Glasserman, Gradient Estimation Via Perturbation Analysis (Springer, New York, 1990).
9. S. Asmussen and P. Glynn, Stochastic Simulation: Algorithms and Analysis (Springer, New York, 2007).
10. S. Ethier and T. Kurtz, Markov Processes: Characterization and Convergence (Wiley, New York, 1986).
11. P. Glynn, Commun. ACM 33, 75 (1990). 10.1145/84537.84552
12. R. Rubinstein and A. Shapiro, Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method (Wiley, New York, 1993).
13. M. Rathinam and H. E. Samad, J. Comput. Phys. 224, 897 (2007). 10.1016/j.jcp.2006.10.034
14. J. Vilar, H. Kueh, N. Barkai, and S. Leibler, Proc. Natl. Acad. Sci. U.S.A. 99, 5988 (2002). 10.1073/pnas.092133899 (Pubitemid 34493252)
15. T. Gardner, C. Cantor, and J. Collins, Nature (London) 403, 339 (2000). 10.1038/35002131 (Pubitemid 30062394)
16. B. P. Flannery, W. H. Press, S. A. Teukolsky, and W. Vetterling, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge University Press, New York, 1992).
17. P. Bremaud, Point Processes and Queues: Martingale Dynamics (Springer Verlag, New York, 1981).
18. P. B. Warren and R. J. Allen, Trajectory reweighting schemes in stochastic modeling: Determination of steady-state parameter sensitivity coefficients (unpublished).