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Microtubules are inherently dynamic sub-cellular filamentuous polymers thatare spatially organized within the cell by motor proteins which cross-link andmove microtubules. In-vitro microtubule motility assays, in which motorsattached to a surface move microtubules along it, have been used traditionallyto study motor function. However, the way in which microtubule-microtubuleinteractions affect microtubule movement remains largely unexplored. To addressthis question, time-lapse image series of in-vitro microtubule motility assayswere obtained using total internal reflection fluorescence (TIRF) microscopy.Categorized as a general problem of multiple object tracking (MOT), particularchallenges arising in this project include low feature diversity, dynamicinstability, sudden changes in microtubules motility patterns, as well as theirinstantaneous appearance/disappearance. This work describes a new applicationof piecewise-stationary multiple motion model Kalman smoother (PMMS) formodeling individual microtubules motility trends. To both evaluate thecapability of this procedure and optimize its hyper-parameters, a large datasetsimulating the series of time-lapse images was used first. Next, we applied itto the sequence of frames from the real data. Results of our analyses provide aquantitative description of microtubule velocity which, in turn, enumerates theoccurrence of microtubule-microtubule interactions per frame.

Multiple Microtubule Tracking in Microscopy Time-Lapse Images Using Piecewise-stationary Multiple Motion Model Kalman Smoother