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Das Influenzavirus infiziert Säugetiere und Vögel. Der erste Schritt im Infektionszyklus ist die Anbindung des Viruses über sein Oberflächenprotein Hämagglutinin (HA) an Zuckerstrukturen auf Epithelzellen des respiratorischen Traktes im Wirtsorganismus. Aus den drei komplementaritätsbestimmenden Regionen (complementarity determining regions, CDRs) der schweren Kette eines monoklonalen Hämagglutinin-bindenden Antikörpers wurden drei lineare Peptide abgeleitet. Die Bindungseigenschaften der drei Peptide wurden experimentell mittels Oberflächenplasmonenresonanzspektroskopie untersucht. Es zeigte sich, dass in Übereinstimmung mit begleitenden Molekulardynamik-Simulationen zwei der drei Peptide (PeB und PeC) analog zur Bindefähigkeit des Antikörpers in der Lage sind, Influenzaviren vom Stamm X31 (H3N2 A/Aichi/2/1968) zu binden. Die Interaktion des Peptids PeB, welches potentiell mit der konservierten Rezeptorbindestelle im HA interagiert, wurde anschließend näher charakterisiert. Die Detektion der Influenzaviren war unter geeigneten Immobilisationsbedingungen im diagnostisch relevanten Bereich möglich. Die Spezifität der PeB-Virus-Bindung wurde mittels geeigneter Kontrollen auf der Seite des Analyten und des Liganden nachgewiesen. Des Weiteren war das Peptid PeB in der Lage die Bindung von X31-Viren an Mimetika seines natürlichen Rezeptors zu inhibieren, was die spezifische Interaktion mit der Rezeptorbindungsstelle im Hämagglutinin belegt. Anschließend wurde die Primärsequenz von PeB durch eine vollständige Substitutionsanalyse im Microarray-Format hinsichtlich der Struktur-Aktivitäts-Beziehungen charakterisiert. Dies führte außerdem zu verbesserten Peptidvarianten mit erhöhter Affinität und breiterer Spezifität gegen aktuelle Influenzastämme verschiedener Serotypen (z.B. H1N1/2009, H5N1/2004, H7N1/2013). Schließlich konnte durch Verwendung einer in der Primärsequenz angepassten höher affinen Peptidvariante die Influenzainfektion in vitro inhibiert werden. Damit stellen die vom ursprünglichen Peptid PeB abgeleiteten Varianten Rezeptormoleküle in biosensorischen Testsystemen sowie potentielle Wirkstoffe dar.
Metabolic systems tend to exhibit steady states that can be measured in terms of their concentrations and fluxes. These measurements can be regarded as a phenotypic representation of all the complex interactions and regulatory mechanisms taking place in the underlying metabolic network. Such interactions determine the system's response to external perturbations and are responsible, for example, for its asymptotic stability or for oscillatory trajectories around the steady state. However, determining these perturbation responses in the absence of fully specified kinetic models remains an important challenge of computational systems biology. Structural kinetic modeling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a parameterised representation of the system's Jacobian matrix in which the model parameters encode information about the enzyme-metabolite interactions. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. The parameter space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Because the sampled parameters are equivalent to the elasticities used in metabolic control analysis (MCA), the results are easy to interpret biologically. In this project, the SKM framework was extended by several novel methodological improvements. These improvements were evaluated in a simulation study using a set of small example pathways with simple Michaelis Menten rate laws. Afterwards, a detailed analysis of the dynamic properties of the neuronal TCA cycle was performed in order to demonstrate how the new insights obtained in this work could be used for the study of complex metabolic systems. The first improvement was achieved by examining the biological feasibility of the elasticity combinations created during Monte Carlo sampling. Using a set of small example systems, the findings showed that the majority of sampled SK-models would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion was formulated that mitigates such infeasible models and the application of this criterion changed the conclusions of the SKM experiment. The second improvement of this work was the application of supervised machine-learning approaches in order to analyse SKM experiments. So far, SKM experiments have focused on the detection of individual enzymes to identify single reactions important for maintaining the stability or oscillatory trajectories. In this work, this approach was extended by demonstrating how SKM enables the detection of ensembles of enzymes or metabolites that act together in an orchestrated manner to coordinate the pathways response to perturbations. In doing so, stable and unstable states served as class labels, and classifiers were trained to detect elasticity regions associated with stability and instability. Classification was performed using decision trees and relevance vector machines (RVMs). The decision trees produced good classification accuracy in terms of model bias and generalizability. RVMs outperformed decision trees when applied to small models, but encountered severe problems when applied to larger systems because of their high runtime requirements. The decision tree rulesets were analysed statistically and individually in order to explore the role of individual enzymes or metabolites in controlling the system's trajectories around steady states. The third improvement of this work was the establishment of a relationship between the SKM framework and the related field of MCA. In particular, it was shown how the sampled elasticities could be converted to flux control coefficients, which were then investigated for their predictive information content in classifier training. After evaluation on the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle with respect to their intrinsic mechanisms responsible for stability or instability. The findings showed that several elasticities were jointly coordinated to control stability and that the main source for potential instabilities were mutations in the enzyme alpha-ketoglutarate dehydrogenase.