Abstract
The use of engineered nanopores as sensing elements for chemical and biological agents is a rapidly developing area. The distinct signatures of nanopore-nanoparticle lend themselves to statistical analysis. As a result, processing of signals from these sensors is attracting a lot of attention. In this paper we demonstrate a neural network approach to classify and interpret nanopore and ion-channel signals.
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Coulter, W.H.: Means for Counting Particles Suspended in a Fluid, U.S. Patent Number 2656508 (1953)
Henriquez, R.R., Ito, T., Sun, L., Crooks, R.M.: The resurgence of Coulter counting for analyzing nanoscale objects. Analyst 129, 478–482 (2004)
Heng, J.B., Ho, C., Kim, T., Timp, R., Aksimentiev, A., Grinkova, Y.V., Sligar, S., Schulten, K., Timp, G.: Sizing DNA using a nanometer-diameter pore. Biophysical Journal 87, 2905–2911 (2004)
Heng, J.B., Aksimentiev, A., Ho, C., Marks, P., Grinkova, Y.V., Sligar, S., Schulten, K., Timp, G.: Stretching DNA using the electric field in a synthetic nanopore. Nano Letters 5, 1883–1888 (2005)
Han, A.P., Schurmann, G., Mondin, G., Bitterli, R.A., Hegelbach, N.G., de Rooij, N.F., Staufer, U.: Sensing protein molecules using nanofabricated pores. Applied Physics Letters 88 (2006)
Deblois, R.W., Bean, C.P., Wesley, R.K.A.: Electrokinetic Measurements with Submicron Particles and Pores by Resistive Pulse Technique. Journal of Colloid and Interface Science 61, 323–335 (1977)
Mara, A., Siwy, Z., Trautmann, C., Wan, J., Kamme, F.: An asymmetric polymer nanopore for single molecule detection. Nano Letters 4, 497–501 (2004)
Ito, T., Sun, L., Henriquez, R.R., Crooks, R.M.: A Carbon Nanotube-Based Coulter Nanoparticle Counter. Acc. Chem. Res. 37(2), 937–945 (2004)
Petrossian, L.: Cylindrical Solid State Nanopores, Ph. D Thesis, Arizona State University, Tempe, AZ-85287
Bayley, H., Martin, C.R.: Resistive-pulse sensing-From microbes to molecules. Chemical Rev. 100, 2575–2594 (2000)
Braha, O., Gu, L.Q., Zhou, L., Lu, X.F., Cheley, S., Bayley, H.: Simultaneous stochastic sensing of divalent metal ions. Nature Biotechnology 18, 1005–1007 (2000)
Gu, L.Q., Braha, O., Conlan, S., Cheley, S., Bayley, H.: Stochastic sensing of organic analytes by a pore-forming protein Containing a molecular adapter. Nature 398, 686–690 (1999)
Luchian, T., Shin, S.H., Bayley, H.: Single-molecule covalent chemistry with spatially separated reactants. Angewandte Chemie-International Edition 42, 3766–3771 (2003)
Wilk, S.J., Goryll, M., Laws, G.M., Goodnick, S.M., Thornton, T.J., Saraniti, M., Tang, J., Eisenberg, R.S.: Teflon (TM)-coated silicon apertures for supported lipid bilayer membranes. Appl. Phys. Lett. 85(15), 3307–3309 (2004)
McManus, O.B., Blatz, A.L., Magleby, K.L.: Sampling, log binning, fitting, and plotting durations of open and shut intervals from single channels and the effects of noise. Pflugers Arch. 410, 530–553 (1987)
Sigworth, F.J., Sine, S.M.: Data transformations for improved display and fitting of single-channel dwell time histograms. Biophysical Journal 52, 1047–1054 (1987)
Ball, F.G., Kerry, C.J., Ramsey, R.L., Sansom, M.S.P., Usherwood, P.N.R.: The use of dwell time cross-correlation functions to study single-ion channel gating kinetics. Biophysical Journal 54, 309–320 (1988)
Venkataramanan, L., Sigworth, F.J.: Applying Hidden Markov Models to the analysis of single ion channel activity. Biophysical Journal 82, 1930–1942 (2002)
Venkataramanan, L., Walsh, J., Kuc, R., Sigworth, F.: Identification of HMM for ion channel currents-part I: colored noise. IEEE Trans. on Sig. Proc. 46(7), 1901–1915 (1998)
Qin, F., Auerbach, A., Sachs, F.: Hidden Markov modeling for single channel kinetics with filtering and correlated noise. Biophysical Journal 79, 1928–1944 (2000)
Spanias, A., Goodnick, S., Thornton, T., Phillips, S., Wilk, S., Kwon, H.: Signal processing for silicon ion-channel sensors. In: Proc. IEEE SAFE 2007 (2007)
Kwon, H., Knee, P., Spanias, A., Goodnick, S., Thornton, T., Phillips, S.: Transform-domain features for ion-channel sensors. In: Proc. IASTED SPPRA 2008, Paper 599-104 (2008)
Konnanath, B., Knee, P., Spanias, A., Wichern, G.: Classification of Ion-Channel Signals using Neural Networks. In: Proc. IASTED SPPRA 2009, Paper 643-075 (2009)
Jagtiani, A.V., Sawant, R., Carletta, J., Zhe, J.: Wavelet transform-based methods for denoising of Coulter counter signals. Meas. Sci. Technol. 19(6), 1–15 (2008)
Bishop, C.M.: Pattern Recognition for Machine Learning. Springer, Heidelberg (2006)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)
QuB: A software package for Markov analysis of single-molecule kinetics, http://www.qub.buffalo.edu/wiki/index.php/Main\_Page
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
The Spider (SVM Toolbox) version 1.71 for MATLAB, http://www.kyb.mpg.de/bs/people/spider/main.html
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Konnanath, B. et al. (2009). Acquiring and Classifying Signals from Nanopores and Ion-Channels. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_27
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DOI: https://doi.org/10.1007/978-3-642-04277-5_27
Publisher Name: Springer, Berlin, Heidelberg
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