The microtuble-associated protein tau is known to regulate neuronal micro-
tubule dynamics and is involved in several neurodegenerative diseases collec-
tively called tauopathies. Besides the formation of tau-containing aggregates
this group of diseases is characterized by changes on different anatomical lev-
els in the nervous system. Morphological changes in the dendritic arbor of neu-
rons or subcellular compartments can be investigated with microscopy-based
and image informatical methods. Furthermore, the functional processes that
constitute these changes can be predicted with bioinformatical methods and
based on these predictions investigated with biological experiments.
Two different bioinformatical disciplines contribute to the study of neurobio-
logical processes. Due to advances in microscopy and imaging coupled to the
tremendous advances in computer technology, image informatics techniques
and workflows are necessary to analyze the acquired data with greater pre-
cision. The classical bioinformatics on the other hand covers the analysis of
molecular evolution, phylogeny and the prediction of protein function.
This work aims to assist neurobiologists with computational methods in ongo-
ing reasearch questions. The development of computer-assisted or fully auto-
mated workflows for image analysis has been achieved on different levels. A
machine learning algorithm has been trained to determine the density of neu-
rons in tissues. Workflows for analysis of morphological changes of dendritic
arbors, like process thickness or branching pattern, have been implemented.
Existing workflows for dendritic spine analysis have been optimized and the
volume and movement behavior of subcellular compartments like ribonucle-
oparticles have been analyzed. Image analysis workflows have been adapted
for the analysis of molecular distributions after photoactivation. Additionally,
techniques from data mining workflows have been adapted to extract and filter
trajectories from single molecule tracking approaches to assist the inferrence of
biophysical parameters.
Sequence data from public available databases have been collected to recon-
struct tau and other related sequences in a broad range of species to infer phy-
logenetic trees and to perform hidden-Markov-model analysis. Using this ap-
proach it has been possible to illuminate the relations in the MAPT/2/4 family
and predict putative functional sequence motifs for further bioinformatical or
biological investigations.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:repositorium.ub.uni-osnabrueck.de:urn:nbn:de:gbv:700-2016062214567 |
| Date | 22 June 2016 |
| Creators | Sündermann, Frederik |
| Contributors | Prof. Dr. Roland Brandt, Prof. Dr. Armen Mulkidjanian |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf, application/zip |
| Rights | Namensnennung 3.0 Unported, http://creativecommons.org/licenses/by/3.0/ |
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