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Computational Methods for Electron Tomography of Influenza Virus

  • Younes Benkarroum
  • Paul Gottlieb
  • Al Katz
  • Stuart W. Rowland
  • Doris Bucher
  • Gabor T. Herman
Chapter
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Abstract

Influenza is a rapidly changing virus that appears seasonally in the human population. Every year a new strain of the influenza virus appears with the potential to cause a serious global pandemic. Knowledge of the structure and density of the surface proteins is of critical importance in a vaccine candidate. Reconstruction techniques from a series of tilted electron-tomographic projection images provide quantification of surface proteins. Two major categories of reconstruction techniques are transform methods such as weighted backprojection (WBP) and series expansion methods such as the algebraic reconstruction techniques (ART) and the simultaneous iterative reconstruction technique (SIRT). Series expansion methods aim at estimating the object to be reconstructed by a linear combination of some fixed basis functions and they typically estimate the coefficients in such an expansion by an iterative algorithm. The choice of the set of basis functions greatly influences the result of a series expansion method. It has been demonstrated repeatedly that using spherically symmetric basis functions (blobs), instead of the more traditional voxels, results in reconstructions of superior quality, provided that the free parameters that occur in the definition of the family of blobs are appropriately tuned. In this chapter, it is demonstrated that, with the recommended data-processing steps performed on the projection images prior to reconstruction, series expansion methods such as ART (with its free parameters appropriately tuned) will provide 3D reconstructions of viruses from tomographic tilt series that allow reliable quantification of the surface proteins and that the same is not achieved using WBP.

Keywords

Influenza Virus Projection Image Line Integral Algebraic Reconstruction Technique Tilt Axis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The work presented here is currently supported by the National Science Foundation award number DMS-1114901. The authors are grateful to Joachim Frank, Carlos Óscar Sanchez Sorzano, José-María Carazo, and especially Hstau Liao for their advice and help with producing filtered WBP reconstructions.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Younes Benkarroum
    • 1
  • Paul Gottlieb
    • 2
  • Al Katz
    • 3
  • Stuart W. Rowland
    • 1
  • Doris Bucher
    • 4
  • Gabor T. Herman
    • 1
  1. 1.Department of Computer Science, The Graduate CenterCity University of New YorkNew YorkUSA
  2. 2.Department of Microbiology and Immunology, Sophie Davis School of Biomedical EducationThe City College of New YorkNew YorkUSA
  3. 3.Department of PhysicsThe City College of New YorkNew YorkUSA
  4. 4.Department of Microbiology and ImmunologyNew York Medical CollegeValhallaUSA

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