@phdthesis{Nowak2020,
  author    = {Nowak, Jacqueline},
  title     = {Devising computational tools to quantify the actin cytoskeleton and pavement cell shape using network-based approaches},
  school      = {Universit{\"a}t Potsdam},
  pages     = {123},
  year      = {2020},
  abstract  = {Recent advances in microscopy have led to an improved visualization of different cell processes. Yet, this also leads to a higher demand of tools which can process images in an automated and quantitative fashion. Here, we present two applications that were developed to quantify different processes in eukaryotic cells which rely on the organization and dynamics of the cytoskeleton.. In plant cells, microtubules and actin filaments form the backbone of the cytoskeleton. These structures support cytoplasmic streaming, cell wall organization and tracking of cellular material to and from the plasma membrane. To better understand the underlying mechanisms of cytoskeletal organization, dynamics and coordination, frameworks for the quantification are needed. While this is fairly well established for the microtubules, the actin cytoskeleton has remained difficult to study due to its highly dynamic behaviour. One aim of this thesis was therefore to provide an automated framework to quantify and describe actin organization and dynamics. We used the framework to represent actin structures as networks and examined the transport efficiency in Arabidopsis thaliana hypocotyl cells. Furthermore, we applied the framework to determine the growth mode of cotton fibers and compared the actin organization in wild-type and mutant cells of rice. Finally, we developed a graphical user interface for easy usage. Microtubules and the actin cytoskeleton also play a major role in the morphogenesis of epidermal leaf pavement cells. These cells have highly complex and interdigitated shapes which are hard to describe in a quantitative way. While the relationship between microtubules, the actin cytoskeleton and shape formation is the object of many studies, it is still not clear how and if the cytoskeletal components predefine indentations and protrusions in pavement cell shapes. To understand the underlying cell processes which coordinate cell morphogenesis, a quantitative shape descriptor is needed. Therefore, the second aim of this thesis was the development of a network-based shape descriptor which captures global and local shape features, facilitates shape comparison and can be used to evaluate shape complexity. We demonstrated that our framework can be used to describe and compare shapes from various domains. In addition, we showed that the framework accurately detects local shape features of pavement cells and outperform contending approaches. In the third part of the thesis, we extended the shape description framework to describe pavement cell shape features on tissue-level by proposing different network representations of the underlying imaging data.},
  language  = {en}
}
@phdthesis{Durek2008,
  author    = {Durek, Pawel},
  title     = {Comparative analysis of molecular interaction networks : the interplay between spatial and functional organizing principles},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-31439},
  school      = {Universit{\"a}t Potsdam},
  year      = {2008},
  abstract  = {The study of biological interaction networks is a central theme in systems biology. Here, we investigate common as well as differentiating principles of molecular interaction networks associated with different levels of molecular organization. They include metabolic pathway maps, protein-protein interaction networks as well as kinase interaction networks. First, we present an integrated analysis of metabolic pathway maps and protein-protein interaction networks (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzyme complexes. Inspecting high-throughput PIN data, it has been shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In this study, we expanded this line of research to include comparisons of the respective underlying network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and hence might be essential for the structural integrity of several biosynthetic systems. Besides metabolic aspects of PINs, we investigated the characteristic topological properties of protein interactions involved in signaling and regulatory functions mediated by kinase interactions. Characteristic topological differences between PINs associated with metabolism, and those describing phosphorylation networks were revealed and shown to reflect the different modes of biological operation of both network types. The construction of phosphorylation networks is based on the identification of specific kinase-target relations including the determination of the actual phosphorylation sites (P-sites). The computational prediction of P-sites as well as the identification of involved kinases still suffers from insufficient accuracies and specificities of the underlying prediction algorithms, and the experimental identification in a genome-scale manner is not (yet) doable. Computational prediction methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding P-sites. However the recognition of such motifs by the respective kinases is a spatial event. Therefore, we characterized the spatial distributions of amino acid residue types around P-sites and extracted signature 3D-profiles. We then tested the added value of spatial information on the prediction performance. When compared to sequence-only based predictors, a consistent performance gain was obtained. The availability of reliable training data of experimentally determined P-sites is critical for the development of computational prediction methods. As part of this thesis, we provide an assessment of false-positive rates of phosphoproteomic data.},
  language  = {en}
}