Automated Tracking and Modeling of Microtubule Dynamics

M.A. El Saban(University of California, Santa Barbara), Alphan Altınok(University of California, Santa Barbara), Austin Peck(University of California, Santa Barbara), Charles Kenney(University of California, Santa Barbara), Stuart C. Feinstein(University of California, Santa Barbara), L Wilson(University of California, Santa Barbara), Kenneth Rose(University of California, Santa Barbara), B.S. Manjunath(University of California, Santa Barbara)
Unknown
May 25, 2006
10.1109/isbi.2006.1625097
Cited by 33

Abstract

The method of microtubule tracking and dynamics analysis, presented here, improves upon the current means of manual and automated quantification of microtubule behavior. Key contributions are increasing accuracy and data volume, eliminating user bias and providing advanced analysis tools for the discovery of temporal patterns in cellular processes. By tracking the entire length of each resolvable microtubule, as opposed to only the tip, it is possible to boost dynamics studies with positional information that is virtually impossible to collect manually. We demonstrate the method on the analysis of a microtubule dataset, which was manually tracked and analyzed in the study of betaIII-tubulin isoform. Our results show that automated recognition of temporal patterns in cellular processes offers a highly promising potential

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