Title:	Computational Image Analysis, Evolutionary Bioinformatics and Modeling of Molecular Interactions of Tau
Authors:	Sündermann, Frederik
Thesis advisor:	Prof. Dr. Roland Brandt
Thesis referee:	Prof. Dr. Armen Mulkidjanian
Abstract:	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.
URL:	https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2016062214567
Subject Keywords:	Bioinformatik; Bildanalyse; Neurobiologie; Bioinformatics; Image analysis; Neurobiology
Issue Date:	22-Jun-2016
License name:	Namensnennung 3.0 Unported
License url:	http://creativecommons.org/licenses/by/3.0/
Type of publication:	Dissertation oder Habilitation [doctoralThesis]
Appears in Collections:	FB05 - E-Dissertationen

This item is licensed under a Creative Commons License