• DocumentCode
    2652594
  • Title

    Model-convolution approach to modeling fluorescent protein dynamics

  • Author

    Sprague, B.L. ; Gardner, M.K. ; Pearson, C.G. ; Maddox, P.S. ; Bloom, K. ; Salmon, E.D. ; Odde, D.J.

  • Author_Institution
    Lab. of Receptor Biol. & Gene Expression, National Cancer Inst., MD, USA
  • Volume
    2
  • fYear
    2004
  • fDate
    7-10 Nov. 2004
  • Firstpage
    1821
  • Abstract
    Fluorescence microscopy is a popular technique for visualizing protein dynamics in living cells. However, the precise distribution of fluorophores underlying the observed fluorescence is not always obvious, even after deconvolution, particularly when features on a scale of 250 nm or less are of interest In contrast, quantitative models of protein dynamics predict an actual fluorophore distribution. "Model-convolution" is a method that bridges this gap by convolving model-predicted fluorophore location data with the point spread function of the microscope system so that simulated images can be generated and directly compared to experimental images. This article offers a practical guide to model-convolution.
  • Keywords
    biological techniques; biology computing; convolution; deconvolution; fluorescence; image colour analysis; image resolution; microscopy; proteins; fluorescence microscopy; fluorescent protein dynamics modeling; microscope system; model-convolution approach; multicolor imaging; protein dynamics; simulated image; Biological system modeling; Biology; Cells (biology); Cellular networks; Fluorescence; Mathematical model; Microscopy; Predictive models; Proteins; Spatial resolution;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on
  • Print_ISBN
    0-7803-8622-1
  • Type

    conf

  • DOI
    10.1109/ACSSC.2004.1399478
  • Filename
    1399478