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Principles of Single-Channel Kinetic Analysis

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Patch-Clamp Methods and Protocols

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 403))

Summary

Single-channel recording provides molecular insights that are nearly unattainable from macroscopic measurements. Analysis of the data, however, has proven to be a difficult challenge. Early approach relies on the half-amplitude threshold detection to idealize the data into dwell-times, followed by fitting of the duration histograms to resolve the kinetics. More recent analysis exploits explicit modeling of both the channel and noise statistics to improve the idealization accuracy. The dwell-time fitting has also evolved into direct fitting of the dwell-time sequences using the full maximum likelihood approach while taking account of the effects of missed events. Finally, hidden Markov modeling provides a new paradigm in which both the amplitudes and kinetics can be analyzed simultaneously without the need of idealization. The progress in theory, along with the advance in computing power and the development of user-friendly software, has made single-channel analysis, once a specialty task, now readily accessible to a broader community of scientists.

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References

  1. Colquhoun D, Hawkes AG (1977) Relaxation and fluctions of membrane currents that flow through drug-operated channels. Phil Trans R Soc Lond B 199:231–262.

    CAS  Google Scholar 

  2. McManus OB, Weiss DS, Spivak CE, Blatz AL, Magleby KL (1988) Fractal models are inadequate for the kinetics of four different ion channels. Biophys J 54:859–870.

    Article  CAS  PubMed  Google Scholar 

  3. McManus OB, Spivak CE, Blatz AL, Weiss DS, Magleby KL (1989) Fractal models, Markov models, and channel kinetics. Biophys J 55:383–385.

    Article  CAS  PubMed  Google Scholar 

  4. Korn SJ, Horn R (1988) Statistical discremination of fractal and Markov models of single channel gating, Biophys J 54:871–877.

    Article  CAS  PubMed  Google Scholar 

  5. Sansom MSP, Ball FG, Kerry CJ, McGee R, Ramsey RL, Usherwood PNR (1989) Markov, fractal, diffusion, and related models of ion channel gating. Biophys J 56:1229–1243.

    Article  CAS  PubMed  Google Scholar 

  6. Kienker P (1989) Equivalence of aggregated Markov models of ion-channel gating. Proc. R Soc Lond B 236:269–309.

    Article  CAS  PubMed  Google Scholar 

  7. Colquhoun D, Hawkes AG (1981) On the Stochastic Properties of Single Ion Channels. Proceedings of the Royal Society of London Series B-Biological Sciences, 211:205–235.

    Google Scholar 

  8. Fredkin DR, Montal M, Rice JA (1985) Identification of Aggregated Markovian Models: Application to the Nicotinic Acetylcholine Receptor, pp. 269–289. Proc of the Berkeley Conference in Honor of Jerzy Neymann and Jack Kiefer, Belmont, CA: Wadsworth.

    Google Scholar 

  9. Labarca P, Rice JA, Fredkin DR, Montal M (1985) Kinetic Analysis of Channel Gating: Application to the Cholinergic Receptor Channel and the Chloride Channel From Torpedo California, pp. 469–478 Proc of the Berkeley Conference in Honor of Jerzy Neymann and Jack Kiefer, Belmont, CA: Wadsworth.

    Google Scholar 

  10. Qin F, Li L (2004) Model-based fitting of single-channel dwell-time distributions. Biophysical J 86:1657–1671.

    Article  Google Scholar 

  11. Magleby KL, Pallotta BS (1983) Calcium Dependence of Open and Shut Interval Distributions From Calcium-Activated Potassium Channels in Cultured Rat Muscle, J Physiol. 344: pp. 585–604.

    CAS  PubMed  Google Scholar 

  12. Magleby KL, Weiss DS (1990) Identifying kinetic gating mechanisms for ion channels by using two-dimensional distributions of simulated dwell times. Proc R Soc Lond B Biol Sci 241:220–228.

    Article  CAS  Google Scholar 

  13. Magleby KL and Song L (1992) Dependency plots suggest the kinetic structure of ion channels. Proc R Soc Lond B Biol Sci 249:133–142.

    Article  CAS  Google Scholar 

  14. Horn R, Lange K (1983) Estimating kinetic constants from single channel data. Biophys. J 43:207–223.

    Article  CAS  PubMed  Google Scholar 

  15. Ball FG, Sansom MSP (1989) Ion-channel gating mechanisms: model identification and parameter estimation from single channel recordings. Proc R Soc Lond B 236:385–416.

    Article  CAS  PubMed  Google Scholar 

  16. Qin F, Auerbach A, and Sachs F (1996) Estimating single channel kinetic parameters from idealized patch-clamp data containing missed events. Biophys J 70:264–280.

    Article  CAS  PubMed  Google Scholar 

  17. Colquhoun D, Hawkes AG, and Srodzinski K (1996) Joint distributions of apparent open and shut times of single-ion channels and maximum likelihood fitting of mechanisms. Philosophical Transactions of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences, 354:2555–2590.

    Article  Google Scholar 

  18. Qin F, Auerbach A, Sachs F (1997) Maximum likelihood estimation of aggregated Markov processes. Proc R Soc Lond [Biol] 264:375–383.

    Article  CAS  Google Scholar 

  19. Roux B, Sauve R (1985) A general solution to the time interval omission problem applied to single channel analysis. Biophys J 48:149–158.

    Article  CAS  PubMed  Google Scholar 

  20. Blatz AL, Magleby KL (1986) Correcting single channel data for missed events. Biophys J 49:967–980.

    Article  CAS  PubMed  Google Scholar 

  21. Crouzy SC, Sigworth FJ (1990) Yet another approach to the dwell-time omission problem of single-channel analysis. Biophys J 58: 731–743.

    Article  CAS  PubMed  Google Scholar 

  22. Hawkes AG, Jalali A, Colquhoun D (1992) Asymptotic distributions of apparent open times and shut times in a single channel record allowing for the omission of brief events. Phil Trans R Soc Lond B 337:383–404.

    Article  CAS  Google Scholar 

  23. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical Recipes in C. Cambridge: Cambridge University Press.

    Google Scholar 

  24. Kendall MG, Stuart A (1977) The Advanced Theory of Statistics. London: Griffin.

    Google Scholar 

  25. Nijenhuis A, Wilf HS (1978) Combinatorial Algorithms. New York: Academic Press Inc.

    Google Scholar 

  26. Qin F (2004) Restoration of single-channel currents using the segmental k-means method based on hidden Markov modeling. Biophysical J 86:1488–1501.

    Article  CAS  Google Scholar 

  27. Fredkin DR, Rice JA (1992) Maximum likelihood estimation and identification directly from single-channel recordings. Proc R Soc Lond 239:125–132.

    Article  Google Scholar 

  28. Venkataramanan L, Walsh JL, Kuc R, and Sigworth FJ (1998) Identification of hidden Markov models for ion channel currents - Part I: Colored background noise. IEEE Trans. Signal Processing, 46:1901–1915.

    Article  Google Scholar 

  29. Venkataramanan L, Kuc R, and Sigworth FJ (1998) Identification of hidden Markov models for ion channel currents - Part II: State-dependent excess noise. IEEE Trans. Signal Processing, 46:1916–1929.

    Article  Google Scholar 

  30. Venkataramanan L, Kuc R, and Sigworth FJ (2000) Identification of hidden Markov models for ion channel currents - Part III: Bandlimited, sampled data. IEEE Trans. Signal Processing, 48:376–385.

    Article  Google Scholar 

  31. Qin F, Auerbach A, and Sachs F (2000) A direct optimization approach to hidden Markov modeling for single channel kinetics. Biophys J 79:1915–1927.

    Article  CAS  PubMed  Google Scholar 

  32. Qin F, Auerbach A, Sachs F (2000) Hidden Markov modeling for single channel kinetics with filtering and correlated noise. Biophys J 79:1928–1944.

    Article  CAS  PubMed  Google Scholar 

  33. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77:257–286.

    Article  Google Scholar 

  34. Rabiner LR, Wilpon JG, and Juang BH (1986) A segmental k-means training procedure for connected word recognition. AT & T Tech. J 65:21–31.

    Google Scholar 

  35. Forney GD (1973) The Viterbi algorithm. Proc IEEE 61:268–278.

    Google Scholar 

  36. Baum LE, Petrie T, Soules G, and Weiss N (1970) A maximization technique occuring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Stat 41: 164–171.

    Article  Google Scholar 

  37. Dempster AP, Laird NM, and Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat 39:1–38.

    Google Scholar 

  38. Colquhoun D, Hatton CJ, and Hawkes AJ. 2003. The quality of maximum likelihood estimates of ion channel rate constants. J Physiol 547:699–728.

    Article  CAS  PubMed  Google Scholar 

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Authors

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Peter Molnar James J. Hickman

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© 2007 Humana Press Inc.

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Qin, F. (2007). Principles of Single-Channel Kinetic Analysis. In: Molnar, P., Hickman, J.J. (eds) Patch-Clamp Methods and Protocols. Methods in Molecular Biology™, vol 403. Humana Press. https://doi.org/10.1007/978-1-59745-529-9_17

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  • DOI: https://doi.org/10.1007/978-1-59745-529-9_17

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-698-6

  • Online ISBN: 978-1-59745-529-9

  • eBook Packages: Springer Protocols

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