Microtubules are major components of the cytoskeleton and play an important role
in a number of cellular functions such as maintaining cell shape, cell division and transport
of various molecules. Abnormal dynamic behavior of microtubules has been associated
with neuro-degenerative diseases (e.g., Alzheimer) and cancer. Researchers
study the dynamics of microtubules under different experimental conditions including
different drug treatments, and using time sequence images from fluorescence microscopy.
At present the dynamics of microtubules are quantified using simple first
and second-order statistical measures of the length variations of manually tracked microtubules.
The current analysis being mostly done manually, is quite laborious and
time-consuming. Besides, the number of microtubules that one can track with manual
methods is limited. In the first part of the thesis, we propose novel tools for automated
detection and tracking of microtubules. A multiframe graph-based approach is
proposed to tackle tracking issues, and our results demonstrate the robustness of the
proposed approach to occlusions and intersections.
In the second part of the thesis, we propose the use of statistical modeling tools for
a better understanding of the underlying molecular mechanisms of microtubule dynamics.
Prototype models are estimated for various experimental conditions by training hidden Markov models (HMMs) on the microtubule tracking data. Furthermore, these
models are used to quantify similarities between experimental conditions. Additionally,
temporal association rules are derived to characterize frequent patterns in the microtubule
dynamics under different experimental conditions. The extraction of frequent
patterns leads to a better understanding of how an experimental condition, such as the
application of a drug, modulates microtubule dynamics.