  Microtubules are inherently dynamic sub-cellular filamentuous polymers that
are spatially organized within the cell by motor proteins which cross-link and
move microtubules. In-vitro microtubule motility assays, in which motors
attached to a surface move microtubules along it, have been used traditionally
to study motor function. However, the way in which microtubule-microtubule
interactions affect microtubule movement remains largely unexplored. To address
this question, time-lapse image series of in-vitro microtubule motility assays
were obtained using total internal reflection fluorescence (TIRF) microscopy.
Categorized as a general problem of multiple object tracking (MOT), particular
challenges arising in this project include low feature diversity, dynamic
instability, sudden changes in microtubules motility patterns, as well as their
instantaneous appearance/disappearance. This work describes a new application
of piecewise-stationary multiple motion model Kalman smoother (PMMS) for
modeling individual microtubules motility trends. To both evaluate the
capability of this procedure and optimize its hyper-parameters, a large dataset
simulating the series of time-lapse images was used first. Next, we applied it
to the sequence of frames from the real data. Results of our analyses provide a
quantitative description of microtubule velocity which, in turn, enumerates the
occurrence of microtubule-microtubule interactions per frame.
