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Deep Time Series Neural Networks and Fluorescence Data Stream Noise Detection

  • James ObertEmail author
  • Matthew Ferguson
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 857)

Abstract

The advent of fluorescence microscopy and the discovery of green fluorescent protein has resulted in an explosion of fluorescence techniques and the promise of characterizing complex biochemical reactions in the living cell. Fluorescence Correlation Spectroscopy (FCS) which relies on correlation function analysis (CFA) is a powerful tool used for single molecule and single cell measurements that provide new insights into complex biomolecular interactions. FCS time series analysis coupled with the use of deep time-series neural networks provides better insight into a data set than correlation functions alone. Typical correlation functions average over many molecular events to give an average characterization of concentration, mobility, binding or enzymatic processivity. The benefit of coupling CFA with deep-learning is the added ability to perform quantitative measurements in the presence of noise. In this paper, a unique systematic approach for identifying noise in fluorescence data is explained. How classical CFA can be enhanced using a deep time-series neural network is illustrated. How wavelet transforms facilitate signal noise isolation and root cause analysis in both stationary and non-stationary fluorescence signals is also explained. While CFA has been used to study neural networks, this article uniquely combines classical CFA with a time-series neural network for the detection of noise in fluorescence data streams.

Keywords

Time-series neural networks Fluorescence correlation spectroscopy Correlation function analysis 

References

  1. 1.
    Brown, R.H., Twiss, R.Q.: A test of a new type of stellar interferometer on Sirius. Nature 178, 1046–1048 (1956)CrossRefGoogle Scholar
  2. 2.
    Van Hove, L., McVoy, K.W.: Pair distribution functions and scattering phenomena. Phys. Rev. C: Nucl. Phys. 33, 468–476 (1962)zbMATHGoogle Scholar
  3. 3.
    Laser, C., Scattering, L.: Ann. Rev. Phys. Chem. 21, 145–174 (1970)CrossRefGoogle Scholar
  4. 4.
    Elson, E.L., Magde, D.: Fluorescience correlation spectroscopy I. Conceptual basis and theory. Biopolymers 13, 1–27 (1974)CrossRefGoogle Scholar
  5. 5.
    Magde, D., Elson, E.L., Webb, W.W.: Fluorescence correlation spectroscopy II. An experimental realization. Biopolymers 13, 29–61 (1974)CrossRefGoogle Scholar
  6. 6.
    Schwille, P., Haupts, U., Maiti, S., Webb, W.W.: Molecular dynamics in living cells observed by fluorescence correlation spectroscopy with one- and two-photon excitation. Biophys. J. 77, 2251–2265 (1999)CrossRefGoogle Scholar
  7. 7.
    Digman, M.A., Sengupta, P., Wiseman, P.W., Brown, C.M., Horwitz, A.R., Gratton, E.: Fluctuation correlation spectroscopy with a laser-scanning microscope: exploiting the hidden time structure. Biophys. J. 88, L33–L36 (2005)CrossRefGoogle Scholar
  8. 8.
    Digman, M.A., Dalal, R., Horwitz, A.F., Gratton, E.: Mapping the number of molecules and brightness in the laser scanning microscope. Biophys. J. 94, 2320–2332 (2008)CrossRefGoogle Scholar
  9. 9.
    Larson, D.R., Zenklusen, D., Wu, B., Chao, J.A., Singer, R.H.: Real-time observation of transcription initiation and elongation on an endogenous yeast gene. Science 332, 475–478 (2011)CrossRefGoogle Scholar
  10. 10.
    Coulon, A., Ferguson, M.L., de Turris, V., Palangat, M., Chow, C.C., Larson, D.R.: Kinetic competition during the transcription cycle results in stochastic RNA processing. Elife 3 (2014).  https://doi.org/10.7554/elife.03939
  11. 11.
    Morisaki, T., Lyon, K., DeLuca, K.F., DeLuca, J.G., English, B.P., Zhang, Z., et al.: Real-time quantification of single RNA translation dynamics in living cells. Science 352, 1425–1429 (2016)CrossRefGoogle Scholar
  12. 12.
    Berne, B.J., Pecora, R.: Dynamic light scattering: with applications to chemistry, biology, and physics. Courier Corporation (2000)Google Scholar
  13. 13.
    Coulon, A., Larson, D.R.: Fluctuation analysis: dissecting transcriptional kinetics with signal theory. Methods Enzymol. 572, 159–191 (2016)CrossRefGoogle Scholar
  14. 14.
    Levi, V., Ruan, Q., Gratton, E.: 3-D particle tracking in a two-photon microscope: application to the study of molecular dynamics in cells. Biophys. J. 88, 2919–2928 (2005)CrossRefGoogle Scholar
  15. 15.
    Levi, V., Ruan, Q., Plutz, M., Belmont, A.S., Gratton, E.: Chromatin dynamics in interphase cells revealed by tracking in a two-photon excitation microscope. Biophys. J. 89, 4275–4285 (2005)CrossRefGoogle Scholar
  16. 16.
    Yu, J., Xiao, J., Ren, X., Lao, K., Xie, X.S.: Probing gene expression in live cells, one protein molecule at a time. Science 311, 1600–1603 (2006)CrossRefGoogle Scholar
  17. 17.
    Wohland, T., Rigler, R., Vogel, H.: The standard deviation in fluorescence correlation spectroscopy. Biophys. J. 80, 2987–2999 (2001)CrossRefGoogle Scholar
  18. 18.
    Digman, M.A., Gratton, E.: Analysis of diffusion and binding in cells using the RICS approach. Microsci. Res. Tech. 72, 323–332 (2009)CrossRefGoogle Scholar
  19. 19.
    Nickolls, J., Buck, I., Garland, M., Skadron, K.: Scalable parallel programming with CUDA. Queueing Syst. (2008). http://dl.acm.org/citation.cfm?id=1365500
  20. 20.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Bengio, Y., Goodfellow, I.J., Courville, A.: Deep learning. Nature 521, 436–444 (2015). CiteseerCrossRefGoogle Scholar
  22. 22.
    Tahmasebi, P., Hezarkhani, A., Sahimi, M.: Multiple-point geostatistical modeling based on the cross-correlation functions. Comput. Geosci. 16(3), 779–797 (2012)CrossRefGoogle Scholar
  23. 23.
    Caudill, M., Butler, C.: Understanding Neural Networks: Computer Explorations, vols. 1 and 2. The MIT Press, Cambridge (1992)Google Scholar
  24. 24.
    Chang, G.: Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50(1), 81–98 (2010)CrossRefGoogle Scholar
  25. 25.
    Olhede, S.C., Walden, A.T.: Generalized Morse wavelets. IEEE Trans. Signal Process. 50(11), 2661–2670 (2002)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Lilly, J.M., Olhede, S.C.: Higher-order properties of analytic wavelets. IEEE Trans. Signal Process. 57(1), 146–160 (2009)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Lilly, J.M., Olhede, S.C.: On the analytic wavelet transform. IEEE Trans. Inf. Theory 56(8), 4135–4156 (2010)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Lilly, J.M., Olhede, S.C.: Generalized Morse wavelets as a superfamily of analytic wavelets. IEEE Trans. Signal Process. 60(11), 6036–6041 (2012)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Sello, S., Bellazzini, J.: Wavelet cross-correlation analysis of turbulent mixing from large-eddy-simulations. arXiv:physics/0003029v1 (2000)
  30. 30.
    Hagan, M.T., H.B. Demuth, Beale, M.H.: Neural Network Design. PWS Publishing, Boston (1996). Chaps. 11 and 12Google Scholar
  31. 31.
    Billings, S.A., Chen, S., Korenberg, M.J.: Identification of MIMO non-linear systems using a forward-regression orthogonal estimator. Int. J. Control 49, 2157–2189 (1989)CrossRefGoogle Scholar
  32. 32.
    Li, K., Peng, J.-X., Irwin, G.W.: A fast nonlinear model identification method. IEEE Trans. Autom. Control 50(8), 1211–1216 (2005)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Aguirre, L.A., Billings, S.A.: Dynamical effects of overparametrization in nonlinear models. Physica D 80, 26–40 (1995)CrossRefGoogle Scholar
  34. 34.
    Piroddi, L., Spinelli, W.: An identification algorithm for polynomial NARX models based on simulation error minimization. Int. J. Control 76(17), 1767–1781 (2003)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Piroddi, L.: Simulation error minimization methods for NARX model identification. Int. J. Model. Identif. Control. To be publishedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Biophysics DepartmentBoise State UniversityBoiseUSA

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