The cytotoxicity of organic tin compounds makes them interesting for cancer chemotherapy. Organotin chalcogenide clusters release under acidic decomposition not only an organic tin compound but also an even more toxic hydrogen chalcogenide. To enable biocompatibility of those clusters, they must be derivatized with biomolecules. This project aims to synthesize biofunctionalized organotin chalcogenide clusters and to investigate their decomposition behavior.

Mitochondrial protein import is mediated by the presequence translocase of the inner membrane (TIM23 complex). While Tim17 is central for inner membrane translocation, the exact role of Mgr2, a small transmembrane protein, remains unclear. This project aims to define the function of Mgr2 in precursor protein import, membrane insertion, and translocase stability using genetic, biochemical, and structural approaches.

Non-electric actuators offer a promising alternative for sustainable and remote operations. My research focuses on the development of thermal micro-actuators based on thermomagnetic thin films, which harness the intrinsic material property of magnetization loss near the Curie temperature to achieve controlled mechanical motion. Unlike conventional actuators that rely on electrical stimulation, these devices operate by thermal triggering, eliminating the need for continuous electrical input and thereby reducing power consumption.

This project investigates how sulfur-containing amino acids regulate tRNA thiolation in endothelial cells, thereby controlling protein translation during vessel growth. Mapping the human endothelial tRNA thio-epitranscriptome will uncover novel translational control mechanisms and lay the foundation for potential therapeutic strategies targeting pathological angiogenesis.

This project investigates the mutualism between Vachellia trees and Pseudomyrmex ants, focusing on their role in wound care. Using chemical ecology, proteomics, microbiology, and behavioral experiments, we plan to identify relevant wound healing compounds as well as the evolutionary mechanisms that enabled inter-kingdom wound care. This research expands on our concept of the social immune system by expanding it towards the ants’ host in this unique relationship.

This research projec aims to contribute to the development of methods that extract informative and interpretable features from high-dimensional datasets, with a focus on uncovering high-level causally related factors that describe meaningful semantics of the data. This, in turn, can help us gain deeper insights into the representations found within advanced generative models, particularly foundation models and LLMs, with the goal of improving their efficiency and safety.

Amongst the leading causes of retinal dystrophies worldwide is Retinitis pigmentosa, a severe disease often caused by splice variants in the USH2A gene. This project aims to develop a safe CRISPR-based therapeutic strategy for correction of such splicing defects. Using enhanced-deletion nucleases, the disease-causing alterations can be eliminated, hereby restoring correct protein synthesis. The focus lies on safety features as well as the development of an inducible viral delivery system for clinical application.

The research project investigates the influence of sterically demanding NHC-gold(I) complexes on the cyclization of diyne derivatives. The focus is on the synthesis of various sterically demanding NHC-gold(I) complexes and their application in diyne cyclizations, particularly examining the reactivity and selectivity in gold-catalyzed reactions. Further investigations include theoretical calculations and practical applications of the synthesized cyclization products for pharmaceuticals or organic materials.

This project seeks to advance rare disease classification using deep neural networks by addressing key challenges such as limited data and high heterogeneity. We will assess existing models and their representations, investigating how technical variations in medical data affect their characteristics.

We aim to develop artificial neural networks with brain-inspired circuits. Like in the brain, artificial neurons and synapses are built with novel memristors, arranged in a crossbar array. By combining these with ultra-fast photonics, we seek to improve signal processing and matrix-vector multiplications to address limitations from state-of-the-art architectures. This approach aims to advance computing solutions by reducing energy consumption, computational time, and system complexity.