http://hdl.handle.net/10900/42126 2026-08-01T05:28:30Z 2026-08-01T05:28:30Z Witte Paz, Mathias Alexander http://hdl.handle.net/10900/182019 2026-08-01T01:09:08Z 2026-07-31T00:00:00Z

Integrative Computational Analyses Across the Central Dogma: Developing Applications for Prokaryotic Characterization in the Context of Microbial Control Witte Paz, Mathias Alexander The global rise in antibiotic resistance, coupled with the declining discovery of new compounds, has created a crisis that demands innovative strategies for microbial control. Identifying such novel strategies requires a mechanistic understanding of bacterial molecular pathways. The central dogma of molecular biology, expanded by newer findings on the flow of biological information, provides a fundamental roadmap for prokaryotic characterization based on high-throughput experiments and computational approaches. However, capturing this complexity requires integrative and reproducible computational frameworks that enable robust cross-layer analyzes. This thesis addressed this challenge by developing and applying computational methods that support integrative and visual analyses across the central dogma, with focus on microbial characterization for their control. To enhance the interactive characterization of prokaryotic genomes, the visual analytics tool Evidente is introduced, with the goal of bridging the gap between the exploration of single nucleotide polymorphisms (SNP) and the evolutionary context. Unlike traditional visualization tools, Evidente classifies SNPs based on clade-specificity and enables their visualization with metadata, as well as linking them to a functional context. By applying it to bacterial pathogens, this approach demonstrated how genome-scale variation can be interpreted in an evolutionary context to generate functional and phenotypic hypotheses. Recognizing that genomic data alone are insufficient to explain phenotypic diversity, this thesis integrated transcriptomic data through two complementary web-applications: TSSpredator-Web and TSS-Captur. They address the characterization of the transcriptome's architecture and link the genomic with the transcriptomic layer of the central dogma. TSSpredator-Web extends the tool TSSpredator to identify and classify transcription start sites (TSS), and allows exploration of genome-wide TSS maps together with genomic data. Based on TSS maps, the Nextflow-based pipeline TSS-Captur characterizes transcripts starting from unclassified TSS via computational methods for sequence classification, termination site prediction, and analyses of secondary structure and promoter regions. Together, both approaches showed how transcriptomic data can be used for annotation refinement and transcript discovery, bridging the genomic and transcriptomic layer. Lastly, the power of integrative analyses is demonstrated through two systematic studies of bacterial metallophore systems. The first project focused on the characterization of known metallophore mechanisms across the Staphylococcal genus using computational analyses based on genomic data, representing the first comprehensive genus-wide analysis of these systems. Expanding on this topic, the second study investigates how nasal Corynebacterium species exploit metallophores synthesized by Staphylococcus aureus, suggesting a novel way of controlling pathogens. This was achieved by identifying structural homologs of lipoproteins through the development of the Nextflow-based pipeline PRESERVE, and contextualizing the findings with transcriptomic data. By analyzing the putative promoter regions, we gained insight into how these mechanisms are regulated in Corynebacteria. These studies illustrate how the integration of multiple layers of biological data can shed light into the interactions between species, providing knowledge that can be translated into interference of colonization strategies, and be exploited for microbial control. In summary, this thesis describes a methodological framework for integrative computational analyses across the central dogma. By combining reproducible approaches with interactive visual exploration, it illustrates how integrating data from different biological layers can help to generate insights for prokaryotic characterizations. These insights can be used to establish innovative interventions for microbial control, offering a computational pathway to address the escalating challenges of antibiotic resistance.; Der weltweite Anstieg der Antibiotikaresistenz in Verbindung mit der sinkenden Entdeckungsrate neuer Wirkstoffe hat zu einer internationalen Krise geführt, die innovative Strategien zur Bekämpfung von bakteriellen Pathogenen erfordert. Um neue Strategien zu identifizieren, ist es notwendig, die komplexen Mechanismen hinter den molekularen Signalwegen von Prokaryoten zu verstehen. Das zentrale Dogma der Molekularbiologie, erweitert durch neuere Erkenntnisse über den Fluss biologischer Informationen, liefert einen grundlegenden Fahrplan für die prokaryotische Charakterisierung auf der Grundlage von Hochdurchsatz-Experimenten und computergestützten Ansätzen. Um diese Komplexität zu erfassen, sind jedoch integrative Methoden erforderlich, die explorative und reproduzierbare Analysen ermöglichen, um über mehrere Ebenen Erkenntnisse zu gewinnen. Diese Dissertation hat sich dieser Herausforderung gestellt, indem sie integrative und visuelle Analysen über das zentrale Dogma hinweg entwickelt und durchführt. Dafür wurden computergestützte Methoden entwickelt und angewendet, wobei der Schwerpunkt auf der mikrobiellen Charakterisierung für deren Kontrolle liegt. Um die interaktive Charakterisierung prokaryotischer Genome zu verbessern, wurde das Visual Analtytics Tool Evidente eingeführt, mit dem Ziel, die Lücke zwischen der Erforschung von Einzelnukleotid-Polymorphismen (SNP, engl. single nucleotide polymorphism) und dem evolutionären Kontext zu schließen. Im Gegensatz zu herkömmlichen Visualisierungstools klassifiziert Evidente SNPs auf der Grundlage der Kladenspezifität und ermöglicht ihre Visualisierung mit Metadaten sowie ihre Verknüpfung mit funktionalen Annotationen. Durch die Anwendung auf bakterielle Pathogene zeigte dieser Ansatz, wie genomweite Variationen in einem evolutionären Kontext interpretiert werden können, um robuste funktionelle und phänotypische Hypothesen zu generieren. Ausgehend von der Erkenntnis, dass Genomdaten allein nicht ausreichen, um die phänotypische Vielfalt zu erklären, untersuchte diese Arbeit die Integration von Transkriptomdaten durch zwei sich ergänzende Webanwendungen: TSSpredatorWeb und TSS-Captur. Diese Tools befassen sich mit der Charakterisierung der Transkriptomarchitektur und verbinden die Genom- mit der Transkriptomebene des zentralen Dogmas. TSSpredatorWeb erweitert das Tool TSSpredator zur Identifizierung und Klassifizierung von Transkriptionsstartstellen (TSS) und ermöglicht die Untersuchung genomweiter TSS-Karten zusammen mit Genomdaten. Basierend auf TSS-Karten, die auf Nextflow-basierte Pipeline TSS-Captur charakterisiert Transkripte, die von nicht klassifizierten TSS stammen, indem es Methoden für die Sequenzklassifizierung, die Vorhersage von Transkriptionsterminatoren, die Sekundärstrukturanalyse und die Promotoranalyse integriert. Zusammen zeigten beide Ansätze, wie Transkriptomdaten für die Verfeinerung von Annotationen und die Entdeckung von Transkripten verwendet werden können. Dadurch wird eine Brücke zwischen der genomischen und der transkriptomischen Ebene geschlagen. Schließlich wird die Leistungsfähigkeit integrativer Analysen anhand von zwei systematischen Studien zu bakteriellen Metallophorsystemen demonstriert. Das erste Projekt konzentrierte sich auf die Charakterisierung bekannter Metallophormechanismen in der Gattung Staphylococcus mithilfe computergestützter Analysen auf der Grundlage genomischer Daten und stellte die erste umfassende gattungsweite Analyse dieser Systeme dar. In Erweiterung dieses Themas untersuchte die zweite Studie, wie nasale Corynebacterium-Arten die von Staphylococcus aureus synthetisierten Metallophore nutzen, und deutet damit einen neuen Weg zur Bekämpfung von Krankheitserregern an. Hierzu wurden strukturelle Homologe von Lipoproteinen durch die Entwicklung der Nextflow-basierten Pipeline PRESERVE identifiziert und die Ergebnisse mit Transkriptomdaten in Zusammenhang gebracht. Durch die Analyse der mutmaßlichen Promotorregionen wurden Einblicke in die Regulation dieser Mechanismen in Corynebacteria gewonnen. Diese Studien zeigen, dass die Integration mehrerer Ebenen biologischer Daten Aufschluss über die Wechselwirkungen zwischen Arten geben kann. Diese Erkenntnisse lassen sich auf Kolonisationsstrategien übertragen und können somit für die mikrobielle Kontrolle genutzt werden. Zusammenfassend beschreibt diese Dissertation einen methodischen Rahmen für integrative computergestützte Analysen über alle Ebenen des zentralen Dogmas hinweg. Durch die Kombination reproduzierbarer Ansätze mit interaktiver visueller Exploration wird veranschaulicht, wie die Integration von Daten aus verschiedenen biologischen Ebenen dazu beitragen kann, Erkenntnisse für die Charakterisierung von Prokaryoten zu gewinnen. Dieser computergestützte Weg ermöglicht es, Erkenntnisse zu gewinnen, mit denen innovative Maßnahmen zur mikrobiellen Kontrolle entwickelt werden können, um den zunehmenden Herausforderungen der Antibiotikaresistenz zu begegnen.

2026-07-31T00:00:00Z Swaminathan, Arjhun http://hdl.handle.net/10900/182018 2026-08-01T01:01:23Z 2026-07-31T00:00:00Z

Privacy by Design: From Distributed Learning to Post-Deployment Risks Swaminathan, Arjhun Modern data processing activities increasingly rely on sensitive data, from medical images and genomic data to electronic health records, to enable research, discovery, diagnosis, and decision-making. Yet the very capabilities that make these systems powerful also create privacy risks: data must often be shared across institutions for generalization and accuracy, and once models and datasets are released for downstream use, they become long-lived artifacts that others can query, link, or exploit. Privacy by design, the principle that privacy should be a foundational property of any data processing activity rather than a post-incident patch, offers a response to these risks. However, translating this into concrete technical practice remains a challenge, because the right answer depends on where in the processing lifecycle one stands, what is being protected, and what assumptions about adversaries are relevant. This thesis approaches privacy by design as a technical agenda organized around two regimes: pre-deployment and post-deployment. On the pre-deployment side, we develop privacy-preserving methods for computation on distributed data under semi-honest threat models. We introduce randomized-encoding based approaches for scalable kernel learning on medical images and for multi-site genome-wide association studies on quantitative phenotypes. We further show that widely used classical kernels can be realized through quantum feature maps, and introduce a distributed secure quantum architecture for kernel computation, validated on simulated quantum hardware. On the post-deployment side, we study what deployed artifacts reveal and how that exposure can be exploited or mitigated. We introduce a targeted adversarial attack for hard-label black-box image classifiers that leverages edge information from images to accelerate attack progress under strict query budgets, consistently outperforming existing methods in the low-query regime across diverse architectures. We also develop a topological framework for adaptive k-anonymisation for dynamic datasets, enabling incremental updates to anonymised data releases without full recomputation when the underlying data changes. Taken together, the results presented in this thesis demonstrate that privacy by design for data processing activities is not a single technique but a discipline whose realization spans a composition of architectures, threat models, and lifecycle stages.

2026-07-31T00:00:00Z Schubert, Julian Frederik http://hdl.handle.net/10900/182002 2026-08-01T01:03:28Z 2026-07-31T00:00:00Z

Technological Advances in Bioreporter-Based Screening for Mechanism-Informed Antibiotic Discovery in Bacillus subtilis Schubert, Julian Frederik The discovery of new antibacterial agents with potent and well-defined mechanisms of action has become essential for addressing the growing burden of antibiotic resistance. However, traditional screening pipelines remain constrained by low throughput, limited specificity, and labor-intensive dereplication. These limitations highlight the need for new screening strategies that accelerate antibacterial discovery and enable early mechanistic assessment. This dissertation describes the advancement of a bioreporter-based screening platform in Bacillus subtilis that combines mechanism-informed whole-cell screening with an efficient compound discovery and dereplication workflow. For this purpose, a new generation of bioreporters based on the bacterial luciferase system was developed, including a novel bioreporter specific for proteotoxic stress. In parallel, a customized workflow for the discovery and rapid dereplication of antibacterial agents was established, enabling the seamless integration of the bioreporter technology. The first study implemented the compound-resolved bioactivity-based metabolomics pipeline, which combines the bioreporter panel with high-frequency microfractionation onto microfluidic paper-analytical devices and non-targeted LC-MS/MS. This approach enabled high-throughput antibiotic screening and the identification of bioactive compounds from pure compounds, crude extracts, and producer strains, while providing early insights into their mechanisms of action. The second study expanded the mechanistic coverage of the bioreporter panel by developing a sensitive bioreporter that signals proteotoxic stress caused by the accumulation of damaged or misfolded proteins. Validation with an extensive set of antibacterial reference compounds confirmed the high specificity of the bioreporter for antibacterial agents that induce proteotoxic stress and enabled the discovery of several compounds not previously associated with this mechanism. Integration of the bioreporter with the microfractionation workflow facilitated the identification of an extensive molecular network of streptothricin derivatives from the Tübingen collection of actinomycete producer strains, including multiple putatively novel analogues. Overall, this work establishes a versatile, bioreporter-based screening platform for mechanism-informed antibiotic discovery and dereplication. By directly linking bioactivity to compound identity at early stages of screening, this approach enables efficient exploration of natural products and the discovery of new antibacterial agents.

2026-07-31T00:00:00Z Ferdinand, Joseph http://hdl.handle.net/10900/181999 2026-08-01T01:08:47Z 2026-07-31T00:00:00Z

Integrating Motivation Into Immersive Learning: An Experimental Approach to Enhance Virtual Reality’s Effectiveness in Science Education Ferdinand, Joseph This dissertation aims to integrate motivational support into immersive virtual reality (VR) to enhance its effectiveness in science education. VR is considered a unique multimedia learning tool that affords students the possibility to experience and interact with the learning content in a unique way. From a hedonic perspective, the embedded visual representations and images, narrations, and interactive elements in VR can foster students’ motivation (e.g., interest, enjoyment, and engagement), which can positively affect their learning outcomes. From a utilitarian perspective, VR offers unique affordances that traditional classroom teaching cannot and places students at the center of the learning process, thus facilitating the learning of complex concepts that can otherwise be too abstract or difficult to learn. However, despite its unique character, VR’s effectiveness as a learning tool has not yet been established. In this regard, a growing number of empirical studies suggest that VR positively affects students’ interest, motivation, and presence in the virtual learning environment but does not necessarily lead to better academic achievement. One of the reasons often presented to explain these mixed results is that the embedded hedonic elements in virtual learning simulations—which are supposed to enhance students’ motivation—can also distract students, cause overstimulation, and increase cognitive load. Moreover, many currently available virtual learning simulations fail to incorporate instructional and motivational support. According to the cognitive theory of multimedia learning (CTML), students’ motivation plays a fundamental role in learning with multimedia; it fosters generative processing, which is the processing capacity required for students to make sense of the lesson content. At the same time, it has been shown that embedded motivational features (interactions, vivid simulations, and visual representations) and seductive details in VR (interesting elements that are irrelevant for the learning objectives) can take students’ attention away from their learning goals and thus impede effective learning in VR. Hence, the fundamental question is how to keep students focused on their learning goals and engaged in effective processing of the learning content—consequently mitigating the possible negative effects of the hedonic elements on their learning performance. It has been argued that the integration of motivational support into the instructional design of virtual learning simulations (hereafter referred to as the integrative approach) can facilitate appropriate cognitive processing of the learning content. This is because motivation and cognition are directly or indirectly linked, and motivation is an important driving force behind students’ behaviors (e.g., actions and interactions) and mental processes (e.g., attention, selection, organization, and integration of relevant learning content into prior knowledge). On the basis of the CTML and the integrative approach, this dissertation proposes that VR’s effectiveness can be enhanced when motivational cues are integrated into immersive learning environments. The concept of motivation in this dissertation is grounded in two other theoretical perspectives: the technology acceptance model (TAM) and the expectancy-value theory (EVT). Both theoretical perspectives foreground the role of motivation in learning and have practical implications for both the traditional classroom and multimedia learning tools. The TAM is the most parsimonious and widely used model to study and predict students’ attitudes towards multimedia learning tools. The TAM stipulates that students’ perceived usefulness of a given multimedia learning tool such as VR is the most important predictor of its acceptance—in terms of intention to use the learning tool—which constitutes a fundamental prerequisite for its effectiveness. In the classroom setting, EVT also highlights the role of students’ perceived usefulness of the learning activities for their learning performance. Notably, empirical research based on EVT has shown that classical usefulness interventions help to stimulate achievement-related behavior and improve learning outcomes. Most importantly, a large body of research on EVT provides clear and parsimonious methods on how to affect students’ motivation positively. Following this rationale, this dissertation pursues the following three main objectives: The first objective is to test whether integrating classical usefulness interventions (a learning usefulness or a daily-life usefulness intervention) into a virtual learning environment could also enhance VR’s effectiveness. The second is to investigate whether students’ acceptance of VR as a learning tool can be increased through motivational interventions and whether acceptance subsequently fosters students’ learning experience in VR (e.g., perceived presence in VR) and enhances their affective and cognitive learning outcomes. The third is to examine to what extent such usefulness interventions affect students’ visual attention and cognitive processing of the lesson content. To address these objectives, this dissertation applies an experimental approach in three studies and uses multimodal methods (e.g., self-report paper-pencil tests, integrated questionnaires in VR, knowledge tests, and eye-tracking analysis). The data (N = 196) used in these studies were collected from 10 different German high schools (academic track). This dissertation’s original contribution to the field is threefold: First, it considers the use of VR in education from a hedonic and utilitarian perspective and shows that the integration of motivational support into learning instruction in VR can help increase VR’s effectiveness. By using a classical motivational approach (usefulness interventions) to operationalize motivation, it also demonstrates that such parsimonious interventions can also be applied in the context of learning with VR. Second, contrary to almost all studies on TAM, this dissertation uses an experimental approach to test the assumptions of this model. Correspondently, it shows that a learning usefulness intervention can, in fact, increase students’ acceptance of VR as a learning tool, which subsequently affects their learning experience in VR. Third, using an eye-tracking-based approach, it shows that integrating motivation support into the virtual learning simulation positively affects how students process the learning content in VR: The usefulness interventions have a positive effect on students’ visual attention and cognitive processing of the lesson content in VR. From a theoretical perspective, this dissertation supports the integrative approach, according to which the integration of motivation into multimedia learning is fundamental for effective processing of the learning content and provides some evidence concerning the role of motivational support in students’ learning performance in VR. From a practical and educational perspective, this dissertation shows how a parsimonious usefulness intervention can be integrated effectively into virtual simulations and how students’ perception of VR— perceived usefulness and acceptance of VR as a learning tool—can be promoted. This dissertation also provides educators with valuable insights regarding the need for students to be pedagogically prepared and aware of the usefulness of educational tools before using them for learning and teaching purposes. Thereby, this dissertation contributes to our understanding regarding the importance of students’ motivation in learning with VR and how it can enhance VR’s effectiveness in science education. 

2026-07-31T00:00:00Z