Sequential change point detection in molecular dynamics trajectories

Multiscale Modeling and Simulation

Volumn 10, Issue 4, 2012, Pages 1263-1291

  Meerbach, Eike ^a   Latorre, Juan C ^a   Schütte, Christof ^a  

^a FREIE UNIVERSITÄT BERLIN   (Germany)

Author keywords

Change point detection; Fractional Bayes; Molecular dynamics; VAR model

Indexed keywords

AUTO REGRESSIVE MODELS; BAYESIAN MODEL SELECTION; CHANGE POINT DETECTION; FRACTIONAL BAYES; HIGH DIMENSIONAL DATA; MOLECULAR DYNAMICS TRAJECTORIES; SAMPLING PROCEDURES; SEQUENTIAL CHANGES; VECTOR VALUED;

ALGORITHMS;

MOLECULAR DYNAMICS;

EID: 84871844552     PISSN: 15403459     EISSN: 15403467     Source Type: Journal    
DOI: 10.1137/110850621     Document Type: Article

References (45)

1. A. Amadei, A. B. Linssen, and H. J. Berendsen, Essential dynamics of proteins, Proteins, 17 (1993), pp. 412-425.
2. L. Arnold, Stochastic Differential Equations, Wiley, New York, 1974.
3. A. Aue, L. Horváth, M. Hušková, and P. Kokoszka, Change-point monitoring in linear models, Econometrics J., 9 (2006), pp. 373-403.
4. J. O. Berger and L. R. Pericchi, The intrinsic Bayes factor for linear models, in Bayesian Statistics, Vol. 5, Oxford University Press, Oxford, UK, 1996, pp. 25-44.
5. J. M. Bernardo and A. F. M. Smith, Bayesian Theory, John Wiley & Sons, Chichester, 1994.
6. E. J. Bylaska, W. A. de Jong, K. Kowalski, T. P. Straatsma, M. Valiev, D. Wang, E. Aprá, T. L. Windus, S. Hirata, M. T. Hackler, Y. Zhao, P.-D. Fan, R. J. Harrison, M. Dupuis, D. M. A. Smith, J. Nieplocha, V. Tipparaju, M. Krishnan, A. A. Auer, M. Nooijen, E. Brown, G. Cisneros, G. I. Fann, H. Früchtl, J. Garza, K. Hirao, R. Kendall, J. A. Nichols, K. Tsemekhman, K. Wolinski, J. Anchell, D. Bernholdt, P. Borowski, T. Clark, D. Clerc, H. Dachsel, M. Deegan, K. Dyall, D. Elwood, E. Glendening, M. Gutowski, A. Hess, J. Jaffe, B. Johnson, J. Ju, R. Kobayashi, R. Kutteh, Z. Lin, R. Littlefield, X. Long, B. Meng, T. Nakajima, S. Niu, L. Pollack, M. Rosing, G. Sandrone, M. Stave, H. Taylor, G. Thomas, J. van Lenthe, A. Wong, and Z. Zhang, NWChem, a Computational Chemistry Package for Parallel Computers, Version 5.0, Technical report, Pacific Northwest National Laboratory, Richland, WA, 2006.
7. G. Casella and R. L. Berger, Statistical Inference, Wadsworth & Brooks, Pacific Grove, CA, 1990.
8. G. Casella and E. Moreno, Objective Bayesian variable selection, J. Amer. Statist. Assoc., 101 (2006), pp. 157-167.
9. J. D. Chodera, N. Singhal, V. S. Pande, K. A. Dill, and W. C. Swope, Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics, J. Comput. Chem., 126 (2007), 155101.
10. N. Chopin, Dynamic detection of change points in long time series, Ann. Inst. Statist. Math., 59 (2007), pp. 349-366.
11. P. Deuflhard, W. Huisinga, A. Fischer, and Ch. Schütte, Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains, Linear Algebra Appl., 315 (2000), pp. 39-59.
12. P. Deuflhard and M. Weber, Robust Perron cluster analysis in conformation dynamics, Linear Algebra Appl., 398 (2005), pp. 161-184.
13. R. Elber and M. Karplus, Multiple conformational states of proteins: A molecular dynamics analysis of Myoglobin, Science, 235 (1987), pp. 318-321.
14. P. Fearnhead and Z. Liu, On-line inference for multiple changepoint problems, J. R. Stat. Soc. Ser. B Methodol., 69 (2007), pp. 589-605.
15. A. Fischer, S. Waldhausen, I. Horenko, E. Meerbach, and Ch. Schütte, Identification of biomolecular conformations from incomplete torsion angle observations by hidden Markov models, J. Comput. Chem., 28 (2007), pp. 2453-2464.
16. H. Frauenfelder, P. J. Steinbach, and R.D. Young, Conformational relaxation in proteins, Chem. Scripta, 29A (1989), pp. 145-150.
17. F. J. Girón, E. Moreno, and G. Casella, Objective Bayesian analysis of multiple changepoints for linear models, in Bayesian Statistics, Vol. 8, J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West, eds., Oxford University Press, Oxford, UK, 2007, pp. 1-27.
18. A. K. Gupta and D. K. Nagar, Matrix Variate Distributions, Chapman & Hall, Boca Raton, FL, 2000.
19. P. Hänggi, P. Talkner, and M. Borkovec, Reaction-rate theory: Fifty years after Kramers, Rev. Modern Phys., 62 (1990), pp. 252-336.
20. I. Horenko, C. Hartmann, Ch. Schütte, and F. Noé, Data-based parameter estimation of generalized multidimensional Langevin processes, Phys. Rev. E (3), 76 (2007), 016706.
21. I. Horenko and Ch. Schütte, Likelihood-based estimation of multidimensional Langevin models and its application to biomolecular dynamics, Multiscale Model. Simul., 7 (2008), pp. 731-773.
22. S. C. Johnson, Hierarchical clustering schemes, Psychometrika, 2 (1967), pp. 241-254.
23. R. E. Kass and A. E. Raftery, Bayes factors, J. Amer. Statist. Assoc., 90 (1995), pp. 773-795.
24. R. A. Kendall, E. Apra, D. E. Bernholdt, E. J. Bylaska, M. Dupuis, G. I. Fann, R. J. Harrison, J. Ju, J. A. Nichols, J. Nieplocha, T. P. Straatsma, T. L. Windus, and A. T. Wong, High performance computational chemistry: An overview of NWChem a distributed parallel application, Comput. Phys. Comm., 128 (2000), pp. 260-283.
25. O. Lange and H. Grubmüller, Collective Langevin dynamics of conformational motions in proteins, J. Chem. Phys., 124 (2006), 214903.
26. H. Lütkepohl, Introduction to Multiple Time Series Analysis, Springer, Berlin, Heidelberg, 1991.
27. E. Meerbach, Off-and Online Detection of Dynamical Phases in Time Series, Ph.D. thesis, Freie Universität Berlin, Berlin, Germany, 2008.
28. E. Meerbach, E. Dittmer, I. Horenko, and Ch. Schütte, Multiscale modelling in molecular dynamics: Biomolecular conformations as metastable states, in Computer Simulations in Condensed Matter: From Materials to Chemical Biology Volume I, Lecture Notes in Phys. 703, M. Ferrario, G. Ciccotti, and K. Binder, eds., Springer, Berlin, Heidelberg, 2006, pp. 475-497.
29. P. Metzner, Ch. Schütte, and E. Vanden-Eijnden, Illustration of transition path theory on a collection of simple examples, J. Chem. Phys., 125 (2006), 084110.
30. A. Neumaier and T. Schneider, Estimation of parameters and eigenmodes of multivariate autoregressive models, ACM Trans. Math. Software, 27 (2001), pp. 27-57.
31. S. Ni and D. Sun, Bayesian estimates for vector autoregressive models, J. Bus. Econom. Statist., 23 (2005), pp. 105-117.
32. G. U. Nienhaus, J. R. Mourant, and H. Frauenfelder, Spectroscopic evidence for conformational relaxation in Myoglobin, Proc. Nat. Acad. Sci. U.S.A., 89 (1992), pp. 2902-2906.
33. F. Noé, I. Horenko, Ch. Schütte, and J. C. Smith, Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states, J. Chem. Phys., 126 (2007), 155102.
34. A. O'Hagan, Fractional Bayes factors for model comparison, J. R. Statist. Soc. Ser. B Methodol., 57 (1995), pp. 99-138.
35. E. S. Page, A test for a change in a parameter occurring at an unknown point, Biometrika, 42 (1955), pp. 523-527.
36. S. Park, M. K. Sener, D. Lu, and K. Schulten, Reaction paths based on mean first-passage times, J. Chem. Phys., 119 (2003), pp. 1313-1319.
37. L. Perreault, J. Bernier, B. Bobée, and E. Parent, Bayesian change-point analysis in hydrometeorological time series. Part 2. Comparison of change-point models and forecasting, J. Hydrology, 235 (2000), pp. 242-263.
38. Ch. Schütte, A. Fischer, W. Huisinga, and P. Deuflhard, A direct approach to conformational dynamics based on hybrid Monte Carlo, J. Comput. Phys., 151 (1999), pp. 146-168.
39. Ch. Schütte and W. Huisinga, Biomolecular conformations can be identified as metastable sets of molecular dynamics, in Handbook of Numerical Analysis X, Elsevier, Amsterdam, 2003, pp. 699-744.
40. D. J. Spiegelhalter and A. F. M. Smith, Bayes factors for linear and log-linear models with vague prior information, J. R. Statist. Soc. Ser. B Methodol., 44 (1982), pp. 377-387.
41. D. Stalling, M. Westerhoff, and H.-C. Hege, Amira: A highly interactive system for visual data analysis, in The Visualization Handbook, Elsevier, Amsterdam, 2005, pp. 749-767.
42. D. Sun and S. Ni, Bayesian analysis of vector-autoregressive models with noninformative priors, J. Statist. Plann. Inference, 121 (2004), pp. 291-309.
43. E. Vanden-Eijnden and F. A. Tal, Transition state theory: Variational formulation, dynamical corrections, and error estimates, J. Chem. Phys., 123 (2005), 184103.
44. A. F. Voter, Parallel replica method for dynamics of infrequent events, Phys. Rev. B, 57 (1998), pp. R13985-R13988.
45. A. F. Voter, F. Montalenti, and T. C. Germann, Extending the time scale in atomistic simulation of materials, Annu. Rev. Mater. Res., 32 (2002), pp. 321-346.