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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.
Synchronization – the adjustment of rhythms among coupled self-oscillatory systems – is a fascinating dynamical phenomenon found in many biological, social, and technical systems.
The present thesis deals with synchronization in finite ensembles of weakly coupled self-sustained oscillators with distributed frequencies.
The standard model for the description of this collective phenomenon is the Kuramoto model – partly due to its analytical tractability in the thermodynamic limit of infinitely many oscillators. Similar to a phase transition in the thermodynamic limit, an order parameter indicates the transition from incoherence to a partially synchronized state. In the latter, a part of the oscillators rotates at a common frequency. In the finite case, fluctuations occur, originating from the quenched noise of the finite natural frequency sample.
We study intermediate ensembles of a few hundred oscillators in which fluctuations are comparably strong but which also allow for a comparison to frequency distributions in the infinite limit.
First, we define an alternative order parameter for the indication of a collective mode in the finite case. Then we test the dependence of the degree of synchronization and the mean rotation frequency of the collective mode on different characteristics for different coupling strengths.
We find, first numerically, that the degree of synchronization depends strongly on the form (quantified by kurtosis) of the natural frequency sample and the rotation frequency of the collective mode depends on the asymmetry (quantified by skewness) of the sample. Both findings are verified in the infinite limit.
With these findings, we better understand and generalize observations of other authors. A bit aside of the general line of thoughts, we find an analytical expression for the volume contraction in phase space.
The second part of this thesis concentrates on an ordering effect of the finite-size fluctuations. In the infinite limit, the oscillators are separated into coherent and incoherent thus ordered and disordered oscillators. In finite ensembles, finite-size fluctuations can generate additional order among the asynchronous oscillators. The basic principle – noise-induced synchronization – is known from several recent papers. Among coupled oscillators, phases are pushed together by the order parameter fluctuations, as we on the one hand show directly and on the other hand quantify with a synchronization measure from directed statistics between pairs of passive oscillators.
We determine the dependence of this synchronization measure from the ratio of pairwise natural frequency difference and variance of the order parameter fluctuations. We find a good agreement with a simple analytical model, in which we replace the deterministic fluctuations of the order parameter by white noise.
Culture-driven innovation
(2017)
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.
The constantly growing capacity of reconfigurable devices allows simultaneous execution of complex applications on those devices. The mere diversity of applications deems it impossible to design an interconnection network matching the requirements of every possible application perfectly, leading to suboptimal performance in many cases. However, the architecture of the interconnection network is not the only aspect affecting performance of communication. The resource manager places applications on the device and therefore influences latency between communicating partners and overall network load. Communication protocols affect performance by introducing data and processing overhead putting higher load on the network and increasing resource demand. Approaching communication holistically not only considers the architecture of the interconnect, but communication-aware resource management, communication protocols and resource usage just as well. Incorporation of different parts of a reconfigurable system during design- and runtime and optimizing them with respect to communication demand results in more resource efficient communication. Extensive evaluation shows enhanced performance and flexibility, if communication on reconfigurable devices is regarded in a holistic fashion.
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.
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.