Skip to main content

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 24))

Abstract

Uncoupling-coupling Monte Carlo (UCMC) combines uncoupling techniques for finite Markov chains with Markov chain Monte Carlo methodology. UCMC aims at avoiding the typical metastable or trapping behavior of Monte Carlo techniques. From the viewpoint of Monte Carlo, a slowly converging long-time Markov chain is replaced by a limited number of rapidly mixing short-time ones. Therefore, the state space of the chain has to be hierarchically decomposed into its metastable conformations. This is done by means of combining the technique of conformation analysis as recently introduced by the authors, and appropriate annealing strategies. We present a detailed examination of the uncoupling-coupling procedure which uncovers its theoretical background, and illustrates the hierarchical algorithmic approach. Furthermore, application of the UCMC algorithm to the n-pentane molecule allows us to discuss the effect of its crucial steps in a typical molecular scenario.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. J. Berne and J. E. Straub. Novel methods of sampling phase space in the simulation of biological systems. Curr. Opinion in Struct. Biol., 7:181–189, 1997.

    Article  CAS  Google Scholar 

  2. A. Brass, B. J. Pendleton, Y. Chen, and B. Robson. Hybrid Monte Carlo simulations theory and initial comparison with molecular dynamics. Biopolymers, 33:1307–1315, 1993.

    Article  CAS  Google Scholar 

  3. B. W. Church, A. Ulitsky, and D. Shalloway. Macrostate dissection of thermodynamic Monte Carlo integrals. In D.M. Ferguson, J.I. Siepmann and D.G. Truhlar, editors, Monte Carlo Methods in Chemical Physics, volume 105 of Advances in Chemical Physics. J. Wiley & Sons, New York, 1999.

    Google Scholar 

  4. M. Dellnitz and O. Junge. On the approximation of complicated dynamical behavior. SIAM J. Num. Anal, 36(2):491–515, 1999.

    Article  Google Scholar 

  5. P. Deuflhard, W. Huisinga, A. Fischer, and C. Schütte. Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains. Lin. Alg. Appl, 315:39–59, 2000.

    Article  Google Scholar 

  6. S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth. Hybrid Monte Carlo. Phys. Lett. B, 195(2):216–222, 1987.

    Article  CAS  Google Scholar 

  7. D. M. Ferguson, J. I. Siepmann, and D. G. Truhlar, editors. Monte Carlo Methods in Chemical Physics, volume 105 of Advances in Chemical Physics. Wiley, New York, 1999.

    Google Scholar 

  8. A. M. Ferrenberg and R. H. Swendsen. Optimized Monte Carlo data analysis. Phys. Rev. Lett, 63(12):1195–1197, 1989.

    Article  PubMed  CAS  Google Scholar 

  9. A. Fischer. An uncoupling-coupling technique for Markov chain Monte Carlo methods. Available as ZIB-Report 00–04 via http://www.zib.de/bib/pub/pw, 2000.

    Google Scholar 

  10. A. Fischer, F. Cordes, and C. Schütte. Hybrid Monte Carlo with adaptive temperature in mixed-canonical ensemble: Efficient conformational analysis of RNA. J. Comput. Chem., 19(15):1689–1697, 1998.

    Article  CAS  Google Scholar 

  11. H. Frauenfelder, S. G. Sligar, and P. G. Wolynes. The energy landscapes and motions of proteins. Science, 254:1598–1603, 1991.

    Article  PubMed  CAS  Google Scholar 

  12. T. Galliat, P. Deuflhard, R. Roitzsch, and F. Cordes. Automatic identification of metastable conformations via self-organized neural networks. Available as ZIB-Report 00–51 via http://www.zib.de/bib/pub/pw, 2000.

    Google Scholar 

  13. A. Gelman. Inference and monitoring convergence. In W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, editors, Markov Chain Monte Carlo in Practice, pages 131-143. Chapman & Hall, 1996.

    Google Scholar 

  14. A. Gelman and X.-L. Meng. Simulating normalizing constants: From importance sampling to bridge sampling to path sampling. Statist. Sei., 13(2):163–185, 1998.

    Article  Google Scholar 

  15. A. Gelman and D. B. Rubin. Inference from iterative simulation using multiple sequences (with discussion). Statist. Sei., 7(4):457–511, 1992.

    Article  Google Scholar 

  16. C. J. Geyer. Estimating normalizing constants and reweighting mixtures in Markov chain Monte Carlo. Technical Report 568, School of Statistics, Univ. Minnesota, 1994.

    Google Scholar 

  17. T. A. Halgren. Merck molecular force field I-V. J. Comp. Chem., 17(5,6):490–641, 1996.

    CAS  Google Scholar 

  18. U. H. E. Hansmann, Y. Okamoto, and F. Eisenmenger. Molecular dynamics, Langevin and hybrid Monte Carlo simulations in a multicanonical ensemble. Chem. Phys. Lett, 259:321–330, 1996.

    Article  CAS  Google Scholar 

  19. Y. Iba. Extended ensemble Monte Carlo. ISM Research Memo. No.777, available via http://xxx.lanl.gov/abs/cond-mat/0012323, 2000.

    Google Scholar 

  20. P. J. M. v. Laarhoven and E. H. L. Aarts. Simulated Annealing: Theory and Applications. Reidel, Dordrecht, 1987.

    Google Scholar 

  21. E. Marinari and G. Parisi. Simulated tempering: a new Monte Carlo scheme. Europhys. Lett., 19(6):451–458, 1992.

    CAS  Google Scholar 

  22. X.-L. Meng and W. H. Wong. Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statist. Sinica, 6:831–860, 1996.

    Google Scholar 

  23. CD. Meyer. Stochastic complementation, uncoupling Markov chains, and the theory of nearly reducible systems. SIAM Rev., 31:240–272, 1989.

    Article  Google Scholar 

  24. S. P. Meyn and R. L. Tweedie. Markov Chains and Stochastic Stability. Springer, Berlin, 1993.

    Book  Google Scholar 

  25. J.-P. Ryckaert and A. Bellemans. Molecular dynamics of liquid alkanes. Faraday Discuss., 66:95–106, 1978.

    Article  Google Scholar 

  26. C. Schütte. Conformational Dynamics: Modelling, Theory, Algorithm, and Application to Biomolecules. Habilitation Thesis, Fachbereich Mathematik und Informatik, Freie Universität Berlin, 1998. Available as ZIB-Report SC–99–18 via http://www.zib.de/bib/pub/pw.

    Google Scholar 

  27. C. Schütte, A. Fischer, W. Huisinga, and P. Deuflhard. A direct approach to conformational dynamics based on hybrid Monte Carlo. J. Comput. Phys., 151:146–168, 1999.

    Article  Google Scholar 

  28. C. Schütte, W. Huisinga, and P. Deuflhard. Transfer operator approach to conformational dynamics in biomolecular systems. In B. Fiedler, editor, Ergodic Theory, Analysis, and Efficient Simulation of Dynamical Systems. Springer, 2001.

    Google Scholar 

  29. J. I. Siepmann and D. Frenkel. Configurational-bias Monte Carlo — a new sampling scheme for flexible chains. Mol. Phys., 75:59–70, 1992.

    CAS  Google Scholar 

  30. H. A. Simon and A. Ando. Aggregation of variables in dynamic systems. Econo-metrica, 29(2):111–138, 1961.

    Google Scholar 

  31. L. Tierney. Markov chains for exploring posterior distributions (with discussion). Ann. Statist, 22:1701–1762, 1994.

    Article  Google Scholar 

  32. M. Vendruscolo. Modified configurational bias Monte Carlo method for simulation of polymer systems. J. Chem. Phys., 106(7):2970–2976, 1997.

    Article  CAS  Google Scholar 

  33. F. Yaşar, T. Çelik, B. A. Berg, and H. Meirovitch. Multicanonical procedure for continuum peptide models. J. Comput. Chem., 21(14): 1251–1261, 2000.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fischer, A., Schütte, C., Deuflhard, P., Cordes, F. (2002). Hierarchical Uncoupling-Coupling of Metastable Conformations. In: Schlick, T., Gan, H.H. (eds) Computational Methods for Macromolecules: Challenges and Applications. Lecture Notes in Computational Science and Engineering, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56080-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-56080-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43756-7

  • Online ISBN: 978-3-642-56080-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics