Abstract
Single ion-channel signal of cell membrane is a stochastic ionic current in the order of picoampere (pA). Because of the weakness of the signal, the background noise always dominates in the patch-clamp recordings. The threshold detector is traditionally used to denoise and restore the ionic single channel currents. However, this method cannot work satisfactorily when signal-to-noise ratio is lower. A new approach based on hidden Markov model (HMM) is presented to restore ionic single-channel currents and estimate model parameters under white background noise. In the study, a global optimization method of HMM parameters based on stochastic relaxation (SR) algorithm is used to estimate the kinetic parameters of channel. Then, the ideal channel currents are reconstructed applying Viterbi algorithm from the patch-clamp recordings contaminated by noise. The theory and experiments have shown that the method performs effectively under the low signal-to-noise ratio (SNR<5.0) and has fast parameter convergence, high restoration precision and strong noise robusticity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Qin, F., Auerbach, A., Sachs, F.: Estimating Single Channel Kinetic Parameters from Idealized Patch-clamp Data Containing Missed Events. Biophys. J. 70, 264–280 (1996)
Venkataramanan, L., Sigworth, F.J.: Applying Hidden Markov Models to the Analysis of Single Ion Channel Activity. Biophys. J. 82, 1930–1942 (2002)
Chung, S.H., Moore, J.B., Xia, L.G., Premkumar, Gage, L.S.: Characterization of Single Channel Currents Using Digital Signal Processing Techniques Based on Hidden Markov Models. Proc. R. Soc. Lond. B boil. Sci. 329, 265–285 (1990)
Qin, F., Auerbach, A., Sachs, F.: Hidden Markov Modeling for Signal Channel Kinetics with Filtering and Correlated Noise. Biophys. J. 79, 1928–1944 (2000b)
Logothetis, A., Krishmurthy, V.: Expectation Maximization Algorithm for MAP Estimation of Jump Markov Linear Systems. IEEE Trans. on Signal Processing 47, 1456–1468 (1999)
Qin, F., Auerbach, A., Sachs, F.: A Direct Optimization Approach to Hidden Markov Modeling for Single Channel Kinetics. Biophys. J. 79, 1915–1927 (2000a)
Qin, F., Li, L.: Model-based Fitting of Signal-channel Dwell-time Distributions. Biophys. J. 87, 1571–1657 (2004)
Li, S.X., Tan, J.F., Wei, G.: A Modified Iterative Algorithm for HMM’s Parameters. Journal of Circuits and System 3, 82–85 (1998)
He, Q.H., Lu, Y.Q., Wei, G.: A New Approach for HMM Training. Acta Electronica Sinica 28, 56–59 (2000)
Fang, S.W., Dai, B.Q., Li, X.Y.: A Global Optimization Algorithm for Discrete HMM. Journal of Circuits and Systems 5, 78–81 (2000)
Milescu, L.S., Akk, G., Sachs, F.: Maximum Likelihood Estimation of Ion Channel Kinetics from Macroscopic Currents. Biophys. J. 88, 2494–2515 (2005)
Wu, X.M., Song, C.X., Wang, B.: Hidden Markov Model Used in Protein Sequence Analysis. J. Biomed. Eng. 19, 455–458 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qiao, X.Y., Li, G., Lin, L. (2006). Signal Restoration and Parameters’ Estimation of Ionic Single-Channel Based on HMM-SR Algorithm. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_66
Download citation
DOI: https://doi.org/10.1007/11893257_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)