Model convolution: A computational approach to digital image interpretation

Melissa K. Gardner, Brian L. Sprague, Chad G. Pearson, Benjamin D. Cosgrove, Andrew D. Bicek, Kerry Bloom, E. D. Salmon, David J. Odde

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations


Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called "model-convolution," which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution can-not be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory.

Original languageEnglish (US)
Pages (from-to)163-170
Number of pages8
JournalCellular and Molecular Bioengineering
Issue number2
StatePublished - Jun 2010

Bibliographical note

Funding Information:
This work was supported by the Whitaker Foundation, the National Science Foundation, and the National Institutes of Health. The authors thank John Condeelis and James McNally for stimulating discussions.


  • Deconvolution
  • Fluorescence
  • Microscopy
  • Model-convolution
  • Modeling


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