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Robotic Image Acquisition

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Part of the Computational Biology book series (COBO, volume 25)

Abstract

Protein crystallization is a complex phenomenon requiring thousands of experiments corresponding to different crystallization conditions for successful crystallization. In recent years, high-throughput robotic setups have been developed to automate the protein crystallization experiments, and imaging techniques are used to monitor the crystallization progress. Having an automated system to classify the images according to the crystallization phases can be very useful to crystallographers. This chapter describes the design and implementation of a stand-alone, low-cost, and real-time system for protein crystallization image acquisition and classification with a goal to assist crystallographers in scoring crystallization trials.

Notes

Acknowledgements

The majority of this chapter is Reprinted (adapted) with permission from Crystal Growth & Design 2013 13 (7), Madhav Sigdel, Marc L. Pusey, and Ramazan S. Aygun, 2728–2736. Copyright (2013) American Chemical Society. Some modifications have been made to fit into this book.

References

  1. 1.
    Bern, M., Goldberg, D., Stevens, R. C., & Kuhn, P. (2004). Automatic classification of protein crystallization images using a curve-tracking algorithm. Journal of Applied Crystallography, 37(2), 279–287.CrossRefGoogle Scholar
  2. 2.
    Berry, I. M., Dym, O., Esnouf, R., Harlos, K., Meged, R., Perrakis, A., et al. (2006). Spine high-throughput crystallization, crystal imaging and recognition techniques: current state, performance analysis, new technologies and future aspects. Acta Crystallographica Section D: Biological Crystallography, 62(10), 1137–1149.CrossRefGoogle Scholar
  3. 3.
    Cumbaa, C., & Jurisica, I. (2005). Automatic classification and pattern discovery in high-throughput protein crystallization trials. Journal of Structural and Functional Genomics, 6(2–3), 195–202.CrossRefGoogle Scholar
  4. 4.
    Cumbaa, C. A., & Jurisica, I. (2010). Protein crystallization analysis on the world community grid. Journal of Structural and Functional Genomics, 11(1), 61–69.CrossRefGoogle Scholar
  5. 5.
    Cumbaa, C. A., Lauricella, A., Fehrman, N., Veatch, C., Collins, R., Luft, J., et al. (2003). Automatic classification of sub-microlitre protein-crystallization trials in 1536-well plates. Acta Crystallographica Section D: Biological Crystallography, 59(9), 1619–1627.CrossRefGoogle Scholar
  6. 6.
    Duda, R. O., & Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures. Communications of the ACM, 15(1), 11–15.CrossRefzbMATHGoogle Scholar
  7. 7.
    Forsythe, E., Achari, A., & Pusey, M. L. (2006). Trace fluorescent labeling for high-throughput crystallography. Acta Crystallographica Section D: Biological Crystallography, 62(3), 339–346.CrossRefGoogle Scholar
  8. 8.
    Liu, R., Freund, Y., & Spraggon, G. (2008). Image-based crystal detection: a machine-learning approach. Acta Crystallographica Section D: Biological Crystallography, 64(12), 1187–1195.Google Scholar
  9. 8.
    Luft, J. R., Newman, J., & Snell, E. H. (2014). Crystallization screening: the influence of history on current practice. Structural Biology and Crystallization Communications, 70(7), 835–853.CrossRefGoogle Scholar
  10. 10.
    MATLAB. (2013). version 7.10.0 (R2013a). The MathWorks Inc., Natick.Google Scholar
  11. 9.
    Onzalez, R., & Woods, R. (2008). Digital image processing. Prentice Hall.Google Scholar
  12. 10.
    Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285–296), 23–27.Google Scholar
  13. 11.
    Pan, S., Shavit, G., Penas-Centeno, M., Xu, D.-H., Shapiro, L., Ladner, R., et al. (2006). Automated classification of protein crystallization images using support vector machines with scale-invariant texture and gabor features. Acta Crystallographica Section D: Biological Crystallography, 62(3), 271–279.CrossRefGoogle Scholar
  14. 12.
    Po, M. J., & Laine, A. F. (2008). Leveraging genetic algorithm and neural network in automated protein crystal recognition. In 30th annual international conference of the IEEE engineering in medicine and biology society, 2008. EMBS 2008 (pp. 1926–1929): IEEE.Google Scholar
  15. 13.
    Pusey, M., Forsythe, E., & Achari, A. (2008). Fluorescence approaches to growing macromolecule crystals. In Structural proteomics (pp. 377–385): Springer.Google Scholar
  16. 14.
    Pusey, M. L., Liu, Z.-J., Tempel, W., Praissman, J., Lin, D., Wang, B.-C., et al. (2005). Life in the fast lane for protein crystallization and X-ray crystallography. Progress in Biophysics and Molecular Biology, 88(3), 359–386.CrossRefGoogle Scholar
  17. 15.
    Pusey, M., Barcena, J., Morris, M., Singhal, A., Yuan, Q., & Ng, J. (2015). Trace fluorescent labeling for protein crystallization. Acta Crystallographica Section F: Structural Biology Communications, 71(7), 806–814.Google Scholar
  18. 16.
    Saitoh, K., Kawabata, K., & Asama, H. (2006). Design of classifier to automate the evaluation of protein crystallization states. In Proceedings 2006 IEEE international conference on Robotics and automation, 2006. ICRA 2006 (pp. 1800–1805): IEEE.Google Scholar
  19. 17.
    Saitoh, K., Kawabata, K., Kunimitsu, S., Asama, H., & Mishima, T. (2004). Evaluation of protein crystallization states based on texture information. In Proceedings. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004 (IROS 2004) (Vol. 3, pp. 2725–2730): IEEE.Google Scholar
  20. 18.
    Shapiro, L., & Stockman, G. C. (2001). Computer vision. ed: Prentice Hall.Google Scholar
  21. 19.
    Sigdel, M., Pusey, M. L., & Aygun, R. S. (2013). Real-time protein crystallization image acquisition and classification system. Crystal Growth and Design, 13(7), 2728–2736.Google Scholar
  22. 20.
    Spraggon, G., Lesley, S. A., Kreusch, A., & Priestle, J. P. (2002). Computational analysis of crystallization trials. Acta Crystallographica Section D: Biological Crystallography, 58(11), 1915–1923.CrossRefGoogle Scholar
  23. 21.
    Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining (1st ed.). Boston: Addison-Wesley Longman Publishing Co., Inc.Google Scholar
  24. 22.
    Yang, X., Chen, W., Zheng, Y. F., & Jiang, T. (2006). Image-based classification for automating protein crystal identification. In Intelligent computing in signal processing and pattern recognition (pp. 932–937): Springer.Google Scholar
  25. 23.
    Zhu, X., Sun, S., & Bern, M. (2004). Classification of protein crystallization imagery. In 26th annual international conference of the IEEE engineering in medicine and biology society, 2004. IEMBS’04 (Vol. 1, pp. 1628–1631): IEEE.Google Scholar
  26. 24.
    Zuk, W. M., & Ward, K. B. (1991). Methods of analysis of protein crystal images. Journal of Crystal Growth, 110(1), 148–155.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.iXpressGenes, Inc.HuntsvilleUSA
  2. 2.University of Alabama in HuntsvilleHuntsvilleUSA

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