Gaussian processes for trajectory analysis in microtubule tracking applications

Microtubules play an essential role in many cellular processes whose disrupted functioning is associated with devastating human diseases such as cancer. The discovery and testing of microtubule targeting drugs often involve time-lapse fluorescence microscopy imaging of microtubule plus-end binding proteins and require highly accurate estimation of their dynamic behavior. Although many methods exist nowadays to perform fully automatic particle tracking in such images, their accuracy and precision are inevitably limited due to noise and other imaging artifacts, and this negatively affects the estimation of parameters such as microtubule growth speed. Here we propose a new approach to estimate such parameters based on Gaussian processes. It naturally deals with measurement noise and can be easily initialized from the data itself. Experimental results on both synthetic and real data demonstrate that our approach indeed yields more accurate estimates of microtubule dynamics.