title: Model Convolution: A Computational Approach to Digital Image Interpretation.
abstract: 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 cannot 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.
authors.forename: Melissa K
authors.initials: MK
authors.lastname: Gardner
authors.forename: Brian L
authors.initials: BL
authors.lastname: Sprague
authors.forename: Chad G
authors.initials: CG
authors.lastname: Pearson
authors.forename: Benjamin D
authors.initials: BD
authors.lastname: Cosgrove
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Bicek
authors.forename: Kerry
authors.initials: K
authors.lastname: Bloom
authors.forename: E D
authors.initials: ED
authors.lastname: Salmon
authors.forename: David J
authors.initials: DJ
authors.lastname: Odde
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