Open Access

This thesis focuses on identifying genetic factors associated with human kidney disease progression, with three articles presented. Article I describes the identification of loci associated with UACR through trans-ethnic, European-ancestry-specific, and diabetes-specific meta-analyses. An approximate conditional analysis was performed to identify additional independent UACR-associated variants within identified loci. The genome-wide significance level of 𝛼=5×10−8 is used for both primary GWAS association and conditional analyses. However, unlike primary association tests, conditional tests are limited to specific genomic regions surrounding primary GWAS index signals rather than being applied on a genome-wide scale. In article II, we hypothesized that the application of 𝛼=5×10−8 is overly strict and results in a loss of power. To address this issue, we developed a quasi-adaptive method within a weighted hypothesis testing framework. This method exploits the type I error (𝛼=0.05) by providing less conservative SNP specific 𝛼-thresholds to select secondary signals in conditional analysis. Through simulation studies and power analyses, we demonstrate that the quasi-adaptive method outperforms the established criterion 𝛼=5×10−8 as well as the equal weighting scheme (the Sidak-correction). Furthermore, our method performs well when applied to real datasets and can potentially reveal previously undetected secondary signals in existing data. In article III, we extended our quasi-adaptive method to identify plausible multiple independent signals at each locus (a secondary signal, a tertiary signal, a signal of 4th, and beyond) and applied it to the publically available GWAS meta-analysis to detect additional multiple independent eGFR-associated signals. The improved quasi-adaptive method successfully identified additional novel replicated independent SNPs that would have gone undetected by applying too conservative genome-wide significance level of 𝛼=5× 10−8. Colocalization analysis based on the novel independent signals identified potentially functional genes across the kidney and other tissues. Overall, these articles contribute to the understanding of genetic factors associated with human kidney disease progression and provide novel methods for identifying secondary and multiple independent signals in conditional GWAS analyses.

Gram-negative bacteria secrete lipopolysaccharides (LPS), leading to a host immune response of proinflammatory cytokine secretion. Those proinflammatory cytokines are TNF-α and IFN-γ, which induce the production of indoleamine 2,3-dioxygenase (IDO). IDO production is increased during severe sepsis, and septic shock. High IDO levels are associated with increased mortality. This enzyme catalyzes the degradation of tryptophan (TRP) to kynurenine (KYN) along the kynurenine pathway (KP). KYN is further degraded to kynurenic acid (KYNA). Increased IDO levels accompany with increased levels of KYNA, which is associated with immunoparalysis. Due to its central role, the KP is a potential target of therapeutic intervention. The degradation of TRP to KYN by IDO was intervened by 1-Methyltryptophan (1- MT), which is assumed to inhibit IDO. By administering 1-MT, the survival of 1-MT-treated mice suffering from sepsis increased compared to mice not treated with 1-MT. The levels of downstream metabolites such as KYN and KYNA were expected to be decreased. Surprisingly, in healthy mice and pigs, an increase in KYNA after 1-MT administration was reported. Those unexpected metabolite alterations after 1-MT administration, and the mode of action, were not the focus of recent research. Hence, there is no explanation for KYNA increase, while KYN did not change. This thesis aims to postulate a possible degradation pathway of 1-MT along the KP with the help of ordinary differential equation (ODE) systems. Moreover, the developed ODE models were used to determine the ability of 1-MT to inhibit IDO in vivo. Therefore, a multiplicity of ODE models were developed, including a model of the KP, an extension by lipopolysaccharide (LPS) administration, and 1-MT administration. Moreover, seven ODE models were developed, all considering possible degradation pathways of 1-MT. The most likely degradation pathway was combined with the ODE model of LPS administration, including the inhibitory effects of 1-MT. Those models consist of several dependent equations describing the dynamics of the KP. For each component of the KP, one equation describes the alterations over time. Equations for TRP, KYN, KYNA, and quinolinic acid (QUIN) were developed. Moreover, the alterations of serotonin (SER) were also included. All together belong to the TRP metabolism. They include the degradation of TRP to SER and to KYN, which is further degraded to KYNA and QUIN. Every degradation is catalyzed by an enzyme. Therefore, Michaelis-Menten (MM) equations were used employing the substrate constant Km and the maximal degradation velocity Vmax. To reduce the complexity of parameter calculation, Km values of the different enzymes were fixed to literature values. The remaining parameters of the equations were determined so that the trajectories of the calculated metabolite levels correspond to data. The parameters of different models were determined. To propose a degradation pathway of 1-MT leading to increased KYNA levels, seven models were developed and compared. The most likely model was extended to test whether the inhibitory effects of 1-MT on IDO can be determined. Three different approaches determined the ODE model parameters of the different hypothesis of 1-MT degradation. In the first approach, ODE model parameters were fixed to values fitted to an independent data set. In the second approach, parameters were fitted to a subset of the data set, which was used for simulations of the different hypotheses. The third approach calculated ODE model parameters 100 times without fixed parameters. The parameter set ending up in trajectories of the TRP metabolites, which have the smallest distance to the data, was assumed to be the most likely. The ODE model parameters were fitted to data measured in pigs. Two different experimental models delivered data used in this thesis. The first experimental model activates IDO by LPS administration in pigs. The second one combines the IDO activation by LPS with the administration of 1-MT in pigs. The most likely hypothesis, according to approach 1 was the degradation of 1-MT to KYNA and TRP. For the second data set the most likely one was the direct degradation of 1-MT to KYNA. With approach 2 the most likely degradation pathways were the combination of all degradation pathways and the degradation of 1-MT to TRP and TRP to KYNA. With approach 3 the most likely way of KYNA increase was given by the direct degradation of 1-MT to KYNA. In summary, the three approaches revealed hypothesis 2, the direct degradation of 1-MT to KYNA most frequently. A cell-free assay validated this result. This experiment combined 1-MT or TRP with or without the enzyme kynurenine aminotransferase (KAT). KAT was already shown to degrade TRP directly to KYNA. The levels of TRP, KYN and KYNA were measured. The highest KYNA levels were yielded with an assay adding KAT to 1-MT, corresponding to hypothesis 2. The models describing the inhibitory effects of 1-MT revealed that the model without inhibitory effects of 1-MT on IDO was more likely for all three approaches. The correctness of hypothesis 2 has to be confirmed by further in vitro experiments. It also has to be investigated which reactions promote the degradation of 1-MT to KYNA. The missing inhibitory properties of 1-MT on IDO, determined by the in silico ODE models, align with previous research. It was shown that the saturation of 1-MT was too low, e.g. in pigs, to inhibit IDO efficiently. In this study, the first possible degradation pathway of 1-MT along the KP is proposed. The reliability of the results depends on the quality of the experimental data, and the season, when data were measured. Moreover, the results vary between the different approaches of parameter fitting. Different approaches of parameter fitting have to be included in the analysis to get more evidence for the correctness of the results.

A lot of research data has become available since the outbreak of the COVID-19 pandemic in 2019. Connecting this data is essential for the understanding of the SARS-CoV-2 virus and the fight against the pandemic. Amongst biological and biomedical research data, computational models targeting COVID-19 have been emerging and their number is growing constantly. They are a central part of the field of Systems Biology, which aims to understand the mechanisms and behaviour of biological systems. Model predictions help to understand the mechanisms of the novel coronavirus and the life-threatening disease it is causing. Both biomedical research data and modelling data regarding COVID-19 have previously been stored in separated domain-specific graph databases. MaSyMoS, short for Management System for Models and Simulations, is a graph database for storing simulation studies of biological and biochemical systems. The CovidGraph project integrates research data regarding COVID-19 and the coronavirus family from various data resources in a knowledge graph. In this thesis, we integrate simulation models from MaSyMoS, including models targeting COVID-19, into the CovidGraph. Therefore, we present a concept for the integration of simulation studies and the linkage through ontology terms and reference publications in the CovidGraph. Ultimately, we connect data from the field of systems biology and biomedical research data in a graph database.

This cumulative thesis describes contributions to the field of interpretable machine learning in the healthcare domain. Three research articles are presented that lie at the intersection of biomedical and machine learning research. They illustrate how incorporating latent structure can provide a valuable compression of the information hidden in complex healthcare data. Methodologically, this thesis gives an overview of interpretable machine learning and the discovery of latent structure, including clusters, latent factors, graph structure, and hierarchical structure. Different workflows are developed and applied to two main types of complex healthcare data (cohort study data and time-resolved molecular data). The core result builds on Bayesian networks, a type of probabilistic graphical model. On the application side, we provide accurate predictive or discriminative models focusing on relevant medical conditions, related biomarkers, and their interactions.

Diese Dissertation untersucht Zusammenhänge der spieltheoretischen Begriffe des Nash- und Stackelberg-Gleichgewichts in Differenialspielen im N-Spieler-Fall. Weiterhin werden drei verschiedene Lösungskonzepte für das Finden von Gleichgewichten in 2-Spieler-Differentialspielen vorgestellt. Direkte Methoden aus der nichtlinearen Optimierung, der globalen Optimierung und der optimalen Steuerung werden verwendet, um Nash- und Stackelberg-Gleichgewichte in 2-Spieler-Differentialspielen zu finden. Anhand von Anwendungsbeispielen werden die Methoden getestet, analysiert und ausgewertet. Eine Erweiterung des Verfolgungsspiels von Isaacs auf Beschleunigungskomponenten wird betrachtet. Ein bisher unbekanntes Stackelberg-Gleichgewicht wird im Kapitalismusspiel nach Lancaster numerisch berechnet. Zuletzt wird ein Problem aus der Fischerei modelliert und anhand der eingeführten Methoden gelöst.