Publikationsserver der Universitätsbibliothek Marburg

Titel:Motion patterns of subviral particles: Digital tracking, image data processing and analysis
Autor:Rausch, Andreas Ulrich Leonhard
Weitere Beteiligte: Becker, Stephan (Prof. Dr.) und Schanze, Thomas (Prof. Dr.)
Veröffentlicht:2022
URI:https://archiv.ub.uni-marburg.de/diss/z2022/0182
URN: urn:nbn:de:hebis:04-z2022-01823
DOI: https://doi.org/10.17192/z2022.0182
DDC: Informatik
Titel (trans.):Bewegungsmuster subviraler Partikel: Digitales Tracking, Bilddatenverarbeitung und -analyse
Publikationsdatum:2022-04-28
Lizenz:https://creativecommons.org/licenses/by-nc-sa/4.0

Dokument

Schlagwörter:
Marburgvirus, Objektverfolgung, Bildverarbeitung, Ebolavirus, Linear Assignment Problem, Motion patterns, Nucleocapsid, Mean squared displacement, Datenverarbeitung, Zuordnungsproblem, Automation,, Nukleokapside, Bewegungsmuster, Linear assignment problem, Kalman Filter, Support Vector Machine, Fraktale Dimension, Tracking, Kalman filter, Curvature, Maschinelles Lernen, Machine learning

Summary:
At the Institute of Virology, Philipps-University, Marburg, Germany, currently research on the understanding of the transport mechanisms of Ebola- and Marburgvirus nucleocapsids is carried out. This research demands a profound knowledge about the various motion characteristics of the nucleocapids. The analysis of large amounts of samples by conventional manual evaluation is a laborious task and does not always lead to reproducible and comparable results. In a cooperation between the Institute of Virology, Marburg, and the Institute for Biomedical Engineering, University of Applied Sciences, Giessen, Germany, algorithms are developed and programmed that enable an automatic tracking of subviral particles in fluorescence microscopic image sequences. The algorithms form an interface between the biologic and the algorithmic domain. Furthermore, methods to automatically parameterize and classify subviral particle motions are created. Geometric and mathematical approaches, like curvature-, fractal dimension- and mean squared displacement-determination are applied. Statistical methods are used to compare the measured subviral particle motion parameters between different biological samples. In this thesis, the biological, mathematical and algorithmic basics are described and the state of the art methods of other research groups are presented and compared. The algorithms to track, parameterize, classify and statistically analyze subviral particle tracks are presented in the Methods section. All methods are evaluated with simulated data and/or compared to data validated by a virologist. The methods are applied to a set of real fluorescence microscopic image sequences of Marburgvirus infected live-cells. The Results chapter shows that subviral particle motion can be successfully analyzed using the presented tracking and analysis methods. Furthermore, differences between the subviral particle motions in the analyzed groups could be detected. However, further optimization with manually evaluated data can improve the results. The methods developed in this project enhance the knowledge about nucleocapsid transport and may be valuable for the development of effective antiviral agents to cure Ebola- and Marburgvirus diseases. The thesis concludes with a chapter Discussion and Conclusions.


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