Fluorescently-tagged proteins located on vesicles can fuse with the surface membrane (visualised as a ‘puff’) or undock and return back into the bulk of the cell. Detection and quantitative measurement of these events from time-lapse videos has proven difficult. We propose a novel approach to detect fusion and undocking events by first searching for docked vesicles that ‘disappear’ from the field of view, and then using a diffusion model to classify them as either fusion or undocking events. We can also use the same searching method to identify docking events. We present comparative results against existing algorithms.
CITATION STYLE
Berger, L., Mirmehdi, M., Reed, S., & Tavaré, J. (2012). A diffusion model for detecting and classifying vesicle fusion and undocking events. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7512 LNCS, pp. 329–336). Springer Verlag. https://doi.org/10.1007/978-3-642-33454-2_41
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