Mathematisch-Naturwissenschaftliche Fakultät

Development of an automated RNA capture-SELEX for enriching modular RNA light-up sensors

2026-06-09T09:31:14Z

Development of an automated RNA capture-SELEX for enriching modular RNA light-up sensors Legen, Tjasa This thesis examines recent advances in selecting and functionalizing RNA aptamers for molecular sensing applications. Two related studies have been carried out: developing a robotic platform for automated RNA aptamer selection targeting small molecules and creating a modular approach to design fluorogenic RNA sensors. Together, these studies offer a framework that combines standardized aptamer discovery with adaptable sensor design.
The systematic evolution of ligands by exponential enrichment (SELEX) was adapted to a robotic platform, minimizing manual intervention and enhancing reproducibility. Traditional SELEX often involves immobilizing small-molecule targets, which can alter ligand properties. To address this, capture-SELEX was used, in which RNA libraries are immobilized via hybridization to captureoligodeoxynucleotides (ODNs), and unmodified ligands in solution facilitate the recovery of bound sequences. By optimizing the robotic system, a preferential immobilization strategy for the library was systematically investigated, resulting in more substantial enrichment of binding sequences. The platform completes up to twelve selection cycles in 72 hours and has successfully enriched aptamers for several small molecules, including neomycin B, theophylline, and riboflavin. Interaction analysis using fluorescence polarization and isothermal titration calorimetry confirmed specific binding properties of enriched aptamers, with affinities in the micromolar range. Although the current capture-SELEX protocol used on the robotic system has limitations in the affinity of the enriched aptamers compared to immobilization-based selections, it offers a standardized, high-throughput method for the rapid assessment of the utility of aptamers for small molecules.
In the second part of this work, the capture-SELEX strategy was extended to enable the selection of modular allosteric RNA sensors. RNA libraries were designed to couple ligand-binding aptamers to fluorogenic RNA scaffolds, enabling molecular recognition to be directly translated into fluorescence output. Using this library in capture-SELEX, aptamers that bind thiamine pyrophosphate (TPP) were identified. These aptamers were then fused to Broccoli and its red-shifted variant, Red Broccoli, to develop ligand-responsive sensors. By optimizing the linker and spacer regions, the signal-to-background ratio was improved. The selected sensors demonstrated specificity for TPP and thiamine monophosphate, with minimal response to thiamine or unrelated nucleotides. The modular design facilitated the easy swapping of fluorogenic domains and the adjustment of sensor properties, demonstrating its effectiveness for rapid and efficient development of RNA-based sensors.
Overall, this thesis establishes a scalable framework for RNA aptamer discovery and deployment. By integrating automated selection with modular sensor engineering, the presented approach provides a generalizable strategy for developing RNA-based sensing systems applicable to biosensing, synthetic biology, and molecular diagnostics.

Intuitive Explainable Artificial Intelligence for Molecular Design

2026-06-09T08:00:56Z

Intuitive Explainable Artificial Intelligence for Molecular Design Lamens, Alec With the onset of artificial intelligence (AI), machine learning (ML) approaches have become increasingly embedded into the drug discovery pipeline with the aim of accelerating research and reducing cost. Exemplary applications include quantitative structure-property relationship modeling and de novo design of candidate molecules. However, the practical impact of these ML models has been limited by the opaque nature of their predictions, termed the black-box problem. The proliferation of large-scale public chemical datasets combined with advances in high-performance computing has driven a shift from traditional ML approaches toward more complex deep learning (DL) methods. While powerful, these models further exacerbate the inability to rationalize model outcomes. Therefore, the field of explainable AI (XAI) has attracted substantial interest to improve the transparency of model decisions. Given the wide range of available XAI approaches and lack of established practices, these methods are often applied without careful consideration for their suitability to the task at hand. This represents a problem in drug discovery where interpretable explanations are critical to guide experimental follow-up. To address this issue, this dissertation proposes the adaptation of established XAI approaches to operate on the level of chemical structure and facilitate interpretable ML for molecular design. The first study demonstrates the use of Shapley additive explanations (SHAP) to analyze feature sets driving the correct prediction of compound promiscuity. This is followed by a benchmarking study comparing several methods for the calculation of feature importance values, revealing inconsistencies across approaches that complicate the interpretation of feature-importance explanations. To support chemically meaningful interpretability, SHAP values are then combined with counterfactual reasoning, yielding the SHAP-CF methodology. Thereafter, a novel counterfactual method is introduced that generates candidate counterfactuals through the iterative recombination of molecular cores and substituents. This method is evaluated on a challenging multi-class kinase inhibitor prediction task, demonstrating its capacity to generate large numbers of counterfactuals, enabling in-depth analysis. Next, a closely related XAI concept, contrastive explanations, is adapted for molecular design by introducing rational chemical modifications to test compounds through the exchange of scaffolds or substituents with structural analogues, termed MolCE. This approach is shown to provide chemically relevant contrastive explanations that enable direct causal insights into the structural factors that drive model predictions. Finally, MolAnchor is introduced, a domain-adapted variant of the rule-based Anchors method. By conforming the principles underlying the Anchors framework to operate on retrosynthetically meaningful molecular fragments, MolAnchor produces interpretable fragment-level decision rules. A follow-up study showcases how the if-then formulation of the fragment rules is particularly complementary to causal reasoning. Taken together, the studies presented hereindemonstrate the utility of XAI methods that have been augmented with domain-specific information to provide chemically intuitive explanations for predictive models applied to molecular design.

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations

2026-06-08T13:15:35Z

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations Kumar, Uttam; Yu, Ran; Wenzel, Michael; Demidova, Elena Yang, De-Nian; Xie, Xing; Tseng, Vincent S.; Pei, Jian; Huang, Jen-Wei; Lin, Jerry Chun-Wei Early detection of an epileptic seizure's onset is crucial to reduce the impact of seizures on the patient's health. The Electroencephalogram (EEG) has been widely used in clinical epileptology for continuous, long-term measurement of electrical activity in the brain. Despite numerous EEG-based approaches employing diverse models and feature extraction methods for seizure detection, these methods rarely tackle the more challenging task of early detection of the seizure onset, especially as only a few EEG channels are impacted at the onset, and the seizure evidence is minimal. Furthermore, EEG-based seizure onset detection remains challenging due to the sparse, imbalanced, and noisy data, as well as the complexity posed by the diverse nature of epileptic seizures in patients. In this paper, we propose Co-ReaSON – a novel approach towards early detection of focal seizure onsets by considering the onset-specific increase in spatio-temporal correlations across the EEG channels observed over a range of multimodal EEG feature representations, combined in a ResNet18-based model architecture. Evaluation on a real-world dataset demonstrates that Co-ReaSON outperforms the state-of-the-art baselines in focal seizure onset detection by at least 5 percent points regarding the macro-average F1-score.

A geometric compactification of the moduli space of grafted surfaces

2026-06-09T05:11:59Z

A geometric compactification of the moduli space of grafted surfaces Monti, Andrea Egidio In this thesis, we study the degenerations of complex projective structures on an orientable surface S of genus at least two, aiming to describe a compactification of their moduli space and provide a geometric interpretation of the boundary points. The moduli space PT(S) of complex projective structures admits a parametrisation due to Thurston via grafting: each structure corresponds to a metric on S that is obtained from a hyperbolic one by grafting, namely inserting, flat parts along a measured lamination. This construction yields a homeomorphism PT(S) ≅ T(S) × ML(S), where T(S) is Teichmüller space and ML(S) the space of measured laminations. We refer to the metric surfaces resulting from grafting as grafted surfaces.
We prove that degenerating sequences of grafted surfaces, suitably rescaled, can converge geometrically to half-translation surfaces, that is, Euclidean surfaces with cone singularities. We use the orthogeodesic foliation introduced by Calderon and Farre to analyse this phenomenon, and we construct a bordification of PT(S) whose boundary at infinity is given by the moduli space ℙ_ℂQT(S) of half-translation surfaces up to rotation and rescale. The topology on this bordification is the one induced by a marked version of Gromov-Hausdorff convergence introduced in this work.
We also show that PT(S) embeds into the space of projective geodesic currents and this embedding extends continuously to our bordification too. We describe the whole closure of its image, when embedding PT(S) into projective currents using a novel l¹-variant of the grafted metric. The boundary in this case is described by mixed structures, which have appeared in different forms in the bordification of other moduli spaces of geometric structures on surfaces. As an application, we describe the limits of so-called generalised stretch rays in Teichmüller space.