@phdthesis{Wittenbecher2017,
  author    = {Wittenbecher, Clemens},
  title     = {Linking whole-grain bread, coffee, and red meat to the risk of type 2 diabetes},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-404592},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XII, 194, ii},
  year      = {2017},
  abstract  = {Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles. Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations. Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years. Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect. Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication. Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified. Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk. Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures. Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.},
  language  = {en}
}
@phdthesis{Peter2019,
  author    = {Peter, Franziska},
  title     = {Transition to synchrony in finite Kuramoto ensembles},
  doi       = {10.25932/publishup-42916},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429168},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vi, 93},
  year      = {2019},
  abstract  = {Synchronisation - die Ann{\"a}herung der Rhythmen gekoppelter selbst oszillierender Systeme - ist ein faszinierendes dynamisches Ph{\"a}nomen, das in vielen biologischen, sozialen und technischen Systemen auftritt. Die vorliegende Arbeit befasst sich mit Synchronisation in endlichen Ensembles schwach gekoppelter selbst-erhaltender Oszillatoren mit unterschiedlichen nat{\"u}rlichen Frequenzen. Das Standardmodell f{\"u}r dieses kollektive Ph{\"a}nomen ist das Kuramoto-Modell - unter anderem aufgrund seiner L{\"o}sbarkeit im thermodynamischen Limes unendlich vieler Oszillatoren. {\"A}hnlich einem thermodynamischen Phasen{\"u}bergang zeigt im Fall unendlich vieler Oszillatoren ein Ordnungsparameter den {\"U}bergang von Inkoh{\"a}renz zu einem partiell synchronen Zustand an, in dem ein Teil der Oszillatoren mit einer gemeinsamen Frequenz rotiert. Im endlichen Fall treten Fluktuationen auf. In dieser Arbeit betrachten wir den bisher wenig beachteten Fall von bis zu wenigen hundert Oszillatoren, unter denen vergleichbar starke Fluktuationen auftreten, bei denen aber ein Vergleich zu Frequenzverteilungen im unendlichen Fall m{\"o}glich ist. Zun{\"a}chst definieren wir einen alternativen Ordnungsparameter zur Feststellung einer kollektiven Mode im endlichen Kuramoto-Modell. Dann pr{\"u}fen wir die Abh{\"a}ngigkeit des Synchronisationsgrades und der mittleren Rotationsfrequenz der kollektiven Mode von Eigenschaften der nat{\"u}rlichen Frequenzverteilung f{\"u}r verschiedene Kopplungsst{\"a}rken. Wir stellen dabei zun{\"a}chst numerisch fest, dass der Synchronisationsgrad stark von der Form der Verteilung (gemessen durch die Kurtosis) und die Rotationsfrequenz der kollektiven Mode stark von der Asymmetrie der Verteilung (gemessen durch die Schiefe) der nat{\"u}rlichen Frequenzen abh{\"a}ngt. Beides k{\"o}nnen wir im thermodynamischen Limes analytisch verifizieren. Mit diesen Ergebnissen k{\"o}nnen wir Erkenntnisse anderer Autoren besser verstehen und verallgemeinern. Etwas abseits des roten Fadens dieser Arbeit finden wir außerdem einen analytischen Ausdruck f{\"u}r die Volumenkontraktion im Phasenraum. Der zweite Teil der Arbeit konzentriert sich auf den ordnenden Effekt von Fluktuationen, die durch die Endlichkeit des Systems zustande kommen. Im unendlichen Modell sind die Oszillatoren eindeutig in koh{\"a}rent und inkoh{\"a}rent und damit in geordnet und ungeordnet getrennt. Im endlichen Fall k{\"o}nnen die auftretenden Fluktuationen zus{\"a}tzliche Ordnung unter den asynchronen Oszillatoren erzeugen. Das grundlegende Prinzip, die rauschinduzierte Synchronisation, ist aus einer Reihe von Publikationen bekannt. Unter den gekoppelten Oszillatoren n{\"a}hern sich die Phasen aufgrund der Fluktuationen des Ordnungsparameters an, wie wir einerseits direkt numerisch zeigen und andererseits mit einem Synchronisationsmaß aus der gerichteten Statistik zwischen Paaren passiver Oszillatoren nachweisen. Wir bestimmen die Abh{\"a}ngigkeit dieses Synchronisationsmaßes vom Verh{\"a}ltnis von paarweiser nat{\"u}rlicher Frequenzdifferenz zur Varianz der Fluktuationen. Dabei finden wir eine gute {\"U}bereinstimmung mit einem einfachen analytischen Modell, in welchem wir die deterministischen Fluktuationen des Ordnungsparameters durch weißes Rauschen ersetzen.},
  language  = {en}
}
@phdthesis{Muehlenhoff2017,
  author    = {M{\"u}hlenhoff, Judith},
  title     = {Culture-driven innovation},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-104626},
  school      = {Universit{\"a}t Potsdam},
  pages     = {143},
  year      = {2017},
  abstract  = {This cumulative dissertation deals with the potential of underexplored cultural sources for innovation. Nowadays, firms recognize an increasing demand for innovation to keep pace with an ever-growing dynamic worldwide competition. Knowledge is one of the most crucial sources and resource, while until now innovation has been foremost driven by technology. But since the last years, we have been witnessing a change from technology's role as a driver of innovation to an enabler of innovation. Innovative products and services increasingly differentiate through emotional qualities and user experience. These experiences are hard to grasp and require alignment in innovation management theory and practice. This work cares about culture in a broader matter as a source for innovation. It investigates the requirements and fundamentals for "culture-driven innovation" by studying where and how to unlock cultural sources. The research questions are the following: What are cultural sources for knowledge and innovation? Where can one find cultural sources and how to tap into them? The dissertation starts with an overview of its central terms and introduces cultural theories as an overarching frame to study cultural sources for innovation systematically. Here, knowledge is not understood as something an organization owns like a material resource, but it is seen as something created and taking place in practices. Such a practice theoretical lens inheres the rejection of the traditional economic depiction of the rational Homo Oeconomicus. Nevertheless, it also rejects the idea of the Homo Sociologicus about the strong impact of society and its values on individual actions. Practice theory approaches take account of both concepts by underscoring the dualism of individual (agency, micro-level) and structure (society, macro-level). Following this, organizations are no enclosed entities but embedded within their socio-cultural environment, which shapes them and is also shaped by them. Then, the first article of this dissertation acknowledges a methodological stance of this dualism by discussing how mixed methods support an integrated approach to study the micro- and macro-level. The article focuses on networks (thus communities) as a central research unit within studies of entrepreneurship and innovation. The second article contains a network analysis and depicts communities as central loci for cultural sources and knowledge. With data from the platform Meetup.com about events etc., the study explores which overarching communities and themes have been evolved in Berlin's start up and tech scene. While the latter study was about where to find new cultural sources, the last article addresses how to unlock such knowledge sources. It develops the concept of a cultural absorptive capacity, that is the capability of organizations to open up towards cultural sources. Furthermore, the article points to the role of knowledge intermediaries in the early phases of knowledge acquisition. Two case studies on companies working with artists illustrate the roles of such intermediaries and how they support firms to gain knowledge from cultural sources. Overall, this dissertation contributes to a better understanding of culture as a source for innovation from a theoretical, methodological, and practitioners' point of view. It provides basic research to unlock the potential of such new knowledge sources for companies - sources that so far have been neglected in innovation management.},
  language  = {en}
}
@phdthesis{Giorgi2011,
  author    = {Giorgi, Federico Manuel},
  title     = {Expression-based reverse engineering of plant transcriptional networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-56760},
  school      = {Universit{\"a}t Potsdam},
  year      = {2011},
  abstract  = {Regulation of gene transcription plays a major role in mediating cellular responses and physiological behavior in all known organisms. The finding that similar genes are often regulated in a similar manner (co-regulated or "co-expressed") has directed several "guilt-by-association" approaches in order to reverse-engineer the cellular transcriptional networks using gene expression data as a compass. This kind of studies has been considerably assisted in the recent years by the development of high-throughput transcript measurement platforms, specifically gene microarrays and next-generation sequencing. In this thesis, I describe several approaches for improving the extraction and interpretation of the information contained in microarray based gene expression data, through four steps: (1) microarray platform design, (2) microarray data normalization, (3) gene network reverse engineering based on expression data and (4) experimental validation of expression-based guilt-by-association inferences. In the first part test case is shown aimed at the generation of a microarray for Thellungiella salsuginea, a salt and drought resistant close relative to the model plant Arabidopsis thaliana; the transcripts of this organism are generated on the combination of publicly available ESTs and newly generated ad-hoc next-generation sequencing data. Since the design of a microarray platform requires the availability of highly reliable and non-redundant transcript models, these issues are addressed consecutively, proposing several different technical solutions. In the second part I describe how inter-array correlation artifacts are generated by the common microarray normalization methods RMA and GCRMA, together with the technical and mathematical characteristics underlying the problem. A solution is proposed in the form of a novel normalization method, called tRMA. The third part of the thesis deals with the field of expression-based gene network reverse engineering. It is shown how different centrality measures in reverse engineered gene networks can be used to distinguish specific classes of genes, in particular essential genes in Arabidopsis thaliana, and how the use of conditional correlation can add a layer of understanding over the information flow processes underlying transcript regulation. Furthermore, several network reverse engineering approaches are compared, with a particular focus on the LASSO, a linear regression derivative rarely applied before in global gene network reconstruction, despite its theoretical advantages in robustness and interpretability over more standard methods. The performance of LASSO is assessed through several in silico analyses dealing with the reliability of the inferred gene networks. In the final part, LASSO and other reverse engineering methods are used to experimentally identify novel genes involved in two independent scenarios: the seed coat mucilage pathway in Arabidopsis thaliana and the hypoxic tuber development in Solanum tuberosum. In both cases an interesting method complementarity is shown, which strongly suggests a general use of hybrid approaches for transcript expression-based inferences. In conclusion, this work has helped to improve our understanding of gene transcription regulation through a better interpretation of high-throughput expression data. Part of the network reverse engineering methods described in this thesis have been included in a tool (CorTo) for gene network reverse engineering and annotated visualization from custom transcription datasets.},
  language  = {en}
}
@phdthesis{Gengel2021,
  author    = {Gengel, Erik},
  title     = {Direct and inverse problems of network analysis},
  doi       = {10.25932/publishup-51236},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-512367},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VIII, 102},
  year      = {2021},
  abstract  = {Selfsustained oscillations are some of the most commonly observed phenomena in biological systems. They emanate from non-linear systems in a heterogeneous environment and can be described by the theory of dynamical systems. Part of this theory considers reduced models of the oscillator dynamics by means of amplitudes and a phase variable. Such variables are highly attractive for theoretical and experimental studies. Theoretically these variables correspond to an integrable linearization of the generally non-linear system. Experimentally, there exist well established approaches to extract phases from oscillator signals. Notably, one can define phase models also for networks of oscillators. One highly active field examines effects of non-local coupling among oscillators, which is thought to play a key role in networks with strong coupling. The dissertation introduces and expands the knowledge about high-order phase coupling in networks of oscillators. Mathematical calculations consider the Stuart-Landau oscillator. A novel phase estimation scheme for direct observations of an oscillator dynamics is introduced based on numerics. A numerical study of high-order phase coupling applies a Fourier fit for the Stuart-Landau and for the van-der-Pol oscillator. The numerical approach is finally tested on observation-based phase estimates of the Morris-Lecar neuron. A popular approach for the construction of phases from signals is based on phase demodulation by means of the Hilbert transform. Generally, observations of oscillations contain a small and generic variation of their amplitude. The work presents a way to quantify how much the variations of signal amplitude spoil a phase demodulation procedure. For the ideal case of phase modulated signals, amplitude modulations vanish. However, the Hilbert transform produces artificial variations of the reconstructed amplitude even in this case. The work proposes a novel procedure called Iterative Hilbert Transform Embedding to obtain an optimal demodulation of signals. The text presents numerous examples and tests of application for the method, covering multicomponent signals, observables of highly stable limit cycle oscillations and noisy phase dynamics. The numerical results are supported by a spectral theory of convergence for weak phase modulations.},
  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}
}