Moving average filtering with deconvolution (MAD) for hidden Markov model with filtering and correlated noise.

Correlated noise Deconvolution EM algorithm Filter approximation Hidden Markov models Level-dependent noise MscL Parameter estimation Patch clamp

Journal

European biophysics journal : EBJ
ISSN: 1432-1017
Titre abrégé: Eur Biophys J
Pays: Germany
ID NLM: 8409413

Informations de publication

Date de publication:
May 2019
Historique:
received: 06 05 2018
accepted: 22 04 2019
revised: 14 02 2019
pubmed: 28 4 2019
medline: 14 6 2019
entrez: 28 4 2019
Statut: ppublish

Résumé

Ion channel data recorded using the patch clamp technique are low-pass filtered to remove high-frequency noise. Almanjahie et al. (Eur Biophys J 44:545-556, 2015) based statistical analysis of such data on a hidden Markov model (HMM) with a moving average adjustment for the filter but without correlated noise, and used the EM algorithm for parameter estimation. In this paper, we extend their model to include correlated noise, using signal processing methods and deconvolution to pre-whiten the noise. The resulting data can be modelled as a standard HMM and parameter estimates are again obtained using the EM algorithm. We evaluate this approach using simulated data and also apply it to real data obtained from the mechanosensitive channel of large conductance (MscL) in Escherichia coli. Estimates of mean conductances are comparable to literature values. The key advantages of this method are that it is much simpler and computationally considerably more efficient than currently used HMM methods that include filtering and correlated noise.

Identifiants

pubmed: 31028435
doi: 10.1007/s00249-019-01368-1
pii: 10.1007/s00249-019-01368-1
doi:

Substances chimiques

Escherichia coli Proteins 0
Ion Channels 0
MscL protein, E coli 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

383-393

Subventions

Organisme : National Health and Medical Research Council
ID : APP1047980

Références

Adsad SS, Dekate MV (2015) Relation of Z-transform and Laplace transform in discrete time signal. Int Res J Eng Technol 2:813–815
Aidley DJ, Stanfield PR (1996) Ion channels: molecules in action. Cambridge University Press, New York
Almanjahie IM, Khan RN, Milne RK, Martinac B, Nomura T (2015) Hidden Markov analysis of improved bandwidth mechanosensitive ion channel gating data. Eur Biophys J 44:545–556
doi: 10.1007/s00249-015-1060-7 pubmed: 26233758
Baum LE, Eagon JE (1967) An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bull Am Math Soc 73:360–363
doi: 10.1090/S0002-9904-1967-11751-8
Baum LE, Petrie T, Soules G, Weiss N (1970) A maximisation technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann Math Stat 41:164–171
doi: 10.1214/aoms/1177697196
Brockwell PJ, Davis RA (2006) Time series: theory and methods, 2nd edn. Springer, New York
Chung SH, Moore JB, Xia L, Premkumar LS, Gage PW (1990) Characterization of single channel currents using digital signal processing techniques based on hidden Markov models. Philos Trans R Soc Lond Ser B 329:265–285
doi: 10.1098/rstb.1990.0170
Colquhoun D, Hawkes A (1981) On the stochastic properties of single ion channels. Proc R Soc Lond Ser B 211:205–235
doi: 10.1098/rspb.1981.0003
Colquhoun D, Hawkes A (1997) Relaxation and fluctuations of membrane currents that flow through drug-operated channels. Proc R Soc Lond Ser B 199:231–262
Colquhoun D, Sigworth FJ (2009) Fitting and statistical analysis of single-channel records. In: Sakmann B, Neher E (eds) Single channel recording, 2nd edn. Springer, New York, pp 483–587
De Gunst MCM, Künsch HR, Schouten JG (2001) Statistical analysis of ion channel using hidden Markov models with correlated state-dependent noise and filtering. J Am Stat Assoc 9:805–815
doi: 10.1198/016214501753208519
Dempster AP, Laird NM, Rubin DR (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39:1–22
Devijver PA (1985) Baum’s forward–backward algorithm revisited. Pattern Recognit Lett 3:369–373
doi: 10.1016/0167-8655(85)90023-6
Epstein M, Calderhead B, Girolami M, Sivilotti G (2016) Bayesian statistical inference in ion-channel models with exact missed event correction. Biophys J B 111:338–348
Ernstrom GG, Chalfie M (2002) Genetics of sensory mechanotransduction. Annu Rev Gen 36:411–453
doi: 10.1146/annurev.genet.36.061802.101708
Fredkin DR, Rice JA (2001) Fast evaluation of the likelihood of an HMM: ion channel currents with filtering and coloured noise. IEEE Trans Signal Process 49:625–633
doi: 10.1109/78.905892
French AS, Stockbridge LL (1988) Fractal and Markov behaviour in ion channel kinetics. Can J Physiol Pharmcol 66:967–970
doi: 10.1139/y88-159
Gibb AJ, Colquhoun D (1992) Activation of NDMA receptors by L-glutamte in cells dissociated from adult rate hippocampus. J Physiol 456:143–179
doi: 10.1113/jphysiol.1992.sp019331 pubmed: 1293277 pmcid: 1175676
Gillespie PG, Walker RG (2001) Molecular basis of mechanosensory transduction. Nature 413:194–202
doi: 10.1038/35093011 pubmed: 11557988
Hamill OP, Martinac B (2001) Molecular basis of mechanotransduction in living cells. Physiol Rev 81:685–740
doi: 10.1152/physrev.2001.81.2.685 pubmed: 11274342
Hamill OP, Marty A, Neher E, Sakmann B, Sigworth FJ (1981) Improved patch-clamp techniques for high-resolution current recordings from cells and cell-free membrane patches. Pflügers Arc Eur J Physiol 391:85–100
doi: 10.1007/BF00656997
Hille B (2001) Ionic channels of excitable membranes, 3rd edn. Sinauer Associates Inc, Sunderland
Khan RN (2003) Statistical modelling and analysis of ion channel data based on hidden Markov models and the EM algorithm. Dissertation, University of Western Australia, Perth
Khan RN, Martinac B, Madsen BW, Milne RK, Yeo GF, Edeson RO (2005) Hidden Markov analysis of mechanosensitive ion channel gating. Math Biosci 193:139–158
doi: 10.1016/j.mbs.2004.07.007 pubmed: 15748727
Korn SJ, Horn R (1988) Statistical discrimination of fractal and Markov models for single channel gating. Biophys J 54:871–877
doi: 10.1016/S0006-3495(88)83023-6 pubmed: 2468367 pmcid: 1330395
Laüger P (1985) Ionic channels with conformational substates. Biophys J 47:581–590
doi: 10.1016/S0006-3495(85)83954-0 pubmed: 2410042 pmcid: 1435186
Laüger P (1988) Internal motion in proteins and gating kinetics of ionic channels. Biophys J 53:877–884
doi: 10.1016/S0006-3495(88)83168-0 pubmed: 2456104 pmcid: 1330268
Liebovitch LS (1989) Testing fractal and Markov models of ion channel kinetics. Biophys J 55:373–377
doi: 10.1016/S0006-3495(89)82815-2 pubmed: 2469487 pmcid: 1330481
Liebovitch LS, Fischbarg J, Koniarek JP, Todorova I, Wang M (1987) Fractal model of ion channel kinetics. Biochim Biophys Acta 896:173–180
doi: 10.1016/0005-2736(87)90177-5 pubmed: 2432935
Martinac B (2011) Bacterial mechanosensitive channels as a paradigm for mechanosensory transduction. Cell Physiol Biochem 28:1051–1060
doi: 10.1159/000335842 pubmed: 22178995
McManus OB, Magleby KL (1989) Kinetic time constants independent of previous single-channel activity suggest Markov gating for a large conductance Ca-activated K-channel. J Gen Physiol 94:1037–1070
doi: 10.1085/jgp.94.6.1037 pubmed: 2614371
McManus OB, Weiss DS, Spivak CE, Blatz AL, Magleby KL (1988) Fractal models are inadequate for the kinetics of four different channels. Biophys J 54:859–870
doi: 10.1016/S0006-3495(88)83022-4 pubmed: 2468366 pmcid: 1330394
Michalek S, Lerche H, Wagner M, Mitrović N, Schiebe M, Lehmann-Horn F, Timmer J (1999) On identification of Na+ channel gating schemes using moving-average filtered hidden Markov models. Eur Biophys J 28:605–609
doi: 10.1007/s002490050243 pubmed: 10541799
Milhauser GL, Salpeter EE, Oswald RE (1988) Rate-amplitude correlation from single channel records. Biophys J 54:1165–1168
doi: 10.1016/S0006-3495(88)83051-0
Mourjopoulos JN (1994) Digital equalization of room acoustics. Audio Eng Soc Conv 42:884–900
Petracchi D, Barbi M, Pellegrini M, Simoni A (1991) Use of conditional distributions in the analysis of ion channel recordings. Eur Biophys J 20:31–39
doi: 10.1007/BF00183277 pubmed: 1718733
Petrov E, Rohde PR, Martinac B (2011) Flying-patch patch-clamp study of G22E-MscL mutant under high hydrostatic pressure. Biophys J 100:1635–1641
doi: 10.1016/j.bpj.2011.02.016 pubmed: 21463576 pmcid: 3072613
Proakis JG, Manolakis DG (1996) Digital signal processing: principles, algorithms, and applications. Prentice Hall, Englewood Cliffs
Qin F, Auerbach A, Sachs F (2000) Hidden Markov modeling for single channel kinetics with filtering and correlated noise. Biophys J 79:1928–1944
doi: 10.1016/S0006-3495(00)76442-3 pubmed: 11023898 pmcid: 1301084
Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77:257–285
doi: 10.1109/5.18626
Sansom MSP, Ball FG, Kerry CJ, McGee R, Ramsey RL, Usherwood PNR (1989) Markov, fractal, diffusion and related models of ion channel gating: a comparison with experimental data from two ion channels. Biophys J 56:1229–1243
doi: 10.1016/S0006-3495(89)82770-5 pubmed: 2482085 pmcid: 1280626
Schouten JG (2000) Stochastic modelling of ion channel kinetics. Dissertation, Thomas Stieltjes Institute for Mathematics, Vrije Universiteit, Amsterdam
Sukharev S, Sigurdson WJ, Kung C, Sachs F (1999) Energetic and spatial parameters for gating of the bacterial large conductance mechanosensitive channel, MscL. J Gen Physiol 113:525–539
doi: 10.1085/jgp.113.4.525 pubmed: 10102934 pmcid: 2217166
Sukharev S, Betanzos M, Chiang CS, Guy HR (2001) The gating mechanism of the large mechanosensitive channel MscL. Nature 409:720–724
doi: 10.1038/35055559 pubmed: 11217861
Venkataramanan L, Walsh JL, Kuc R, Sigworth FJ (1998a) Identification of hidden Markov models for ion channel currents—part I: coloured background noise. IEEE Trans Signal Process 46:1901–1915
doi: 10.1109/78.700963
Venkataramanan L, Kuc R, Sigworth FJ (1998b) Identification of hidden Markov models for ion channel currents–part II: state dependent excess noise. IEEE Trans Signal Process 46:1916–1929
doi: 10.1109/78.700964
Xiao R, Xu XZS (2010) Mechanosensitive channels: in touch with Piezo. Curr Biol 20:936–938
doi: 10.1016/j.cub.2010.09.053

Auteurs

Ibrahim M Almanjahie (IM)

Department of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia.
Department of Mathematics, King Khalid University, Abha, 61413, Saudi Arabia.

Ramzan Nazim Khan (RN)

Department of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia. nazim.khan@uwa.edu.au.

Robin K Milne (RK)

Department of Mathematics and Statistics, University of Western Australia, Crawley, WA, 6009, Australia.

Takeshi Nomura (T)

Department of Rehabilitation, Kyushu Nutrition Welfare University, Kitakyushu, 800-029, Japan.

Boris Martinac (B)

Mechanosensory Biophysics Laboratory, Victor Chang Cardiac Research Institute, Darlinghurst, NSW, 2010, Australia.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
1.00
Humans Magnetic Resonance Imaging Brain Infant, Newborn Infant, Premature
Humans Colorectal Neoplasms Biomarkers, Tumor Prognosis Gene Expression Regulation, Neoplastic
Humans Algorithms Software Artificial Intelligence Computer Simulation

Classifications MeSH