Model based dynamics analysis in live cell microtubule images

Background: The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. Results: In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. Conclusion: Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior

Activity analysis in microtubule videos by mixture of hidden markov models

We present an automated method for the tracking and dynamics modeling of microtubules-a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes. 1

Example life history plots from hypothetical MTs showing different behaviors

<p><b>Copyright information:</b></p><p>Taken from "Model based dynamics analysis in live cell microtubule images"</p><p>http://www.biomedcentral.com/1471-2121/8/S1/S4</p><p>BMC Cell Biology 2007;8(Suppl 1):S4-S4.</p><p>Published online 10 Jul 2007</p><p>PMCID:PMC1924509.</p><p></p> Life histories were inspired by [33]. Individual MTs in the top undergo several length excursions, while the MTs in the middle row exhibit an overall growth tendency. The bottom chart shows individual MTs, distinguished by filled and open circles, which are superimposed on the time axis for visual comparison. While both groups of MTs display shortening, the group indicated by the open circles shorten gradually as compared with the rest of the MTs