Conference Agenda

Introduction of research at HMGU, as Program Spokesperson

Osteoarthritis is one of the leading causes of disability and pain worldwide, with over 300 million people affected. Currently no curative treatments are available. A detailed understanding of disease aetiopathology and novel drug targets are therefore urgently needed.

In this talk, I will give an overview of how we have used translational genomics approaches to enhance our understanding of the genetic aetiology of osteoarthritis, shed novel biological insights, and provide a stepping stone for translating genetic associations into osteoarthritis drug development.

Optoacoustic imaging has the unique ability to visualize optical contrast deep in biological tissue dynamically and at high resolution without using ionizing radiation. Clinical translation of the modality into clinical workflows depends not only on advances in instrumentation, but also on computational methods that can cope with limited data, complex image formation, and application-specific biological variability. While AI in imaging is often identified with large-scale deep learning, optoacoustic applications frequently require a broader computational perspective in which learning-based methods are combined with biophysical models and probabilistic inference.

Our group develops such methods for optoacoustic systems on multiple scales spanning chemically specific microscopy, mesoscopic high-resolution imaging of tissues, and macroscopic systems capable of imaging at depths of several centimeters. We combine models of system physics, tissue biology, and disease effects with Bayesian variational inference and use AI selectively where it offers clear benefit, including algorithm acceleration and generative prior modeling. In multispectral optoacoustic tomography (MSOT), we enable high image quality in real time through deep-learning-accelerated reconstruction and denoising, and we advance quantitative optoacoustics through hierarchical Bayesian models and variational inference. In mid-infrared optoacoustic microscopy (MiROM), we reduce acquisition time through sparse measurements, while compensating information loss using spatial correlation priors and uncertainty quantification.

These computational tools act as enabling technologies for translational optoacoustics. They deliver high-quality images on systems, support quantitative and uncertainty-aware readouts, and move advanced sensing and imaging workflows toward clinically relevant timescales. More broadly, our work highlights a practical view of AI in biomedical imaging: not as a purely data-driven replacement for modeling, but as a targeted component within biophysics-informed computational pipelines that bring optoacoustic imaging and sensing closer to clinical impact.

Understanding how complex biological systems give rise to functional health and disease remains a major challenge, particularly in tissues where multiple interacting factors shape emergent behavior. In the human airway, mucociliary clearance is a key defense mechanism whose dysfunction underlies many respiratory diseases, yet its regulation across molecular, cellular, and mechanical scales is poorly understood.

Here, we combine organotypic human airway models with systematic perturbation screens and multimodal readouts, including immunofluorescence staining, functional clearance assays, and single-cell RNA sequencing. Using these data, we are in the process of developing an AI-based predictive framework (CellFlow) that maps how combinations of developmental, inflammatory, and physiological factors regulate epithelial differentiation and clearance function.

This approach will enable prediction and experimental validation of previously untested perturbations, revealing regulatory logic that cannot be inferred from single-factor studies. More broadly, our work aims to demonstrate how AI-integrated experimental systems can transform biological models into predictive platforms for studying disease mechanisms and guiding therapeutic strategies.

The virtual cell to model and predict cellular behavior and perturbation responses is a defining ambition of AI-biology. Two powerful paradigms are driving progress: sequence-based models predict molecular properties bottom-up, whereas transcriptomic approaches represent cell states and forecast trajectories. Yet between both is a layer that neither reliably reaches: the physical, conditional molecular networks that execute molecular programs.

Despite the transformative impact of AlphaFold on structural biology, accurately predicting binary protein-protein interactions remains an unsolved problem. Drawing on recent work I will show where current structure-based AI models systematically fail in interaction prediction, and why large-scale experimental mapping remains indispensable, not as a stopgap, but as the empirical foundation for a biochemistry that models cannot yet replace.

This experimental foundation becomes concrete in the context of microbe-host biology. The gut microbiome is known to influence the trajectory of numerous complex diseases, yet the molecular mechanisms remain largely obscure. We showed that commensal bacteria deploy a molecular machine only known from pathogens to inject bacterial proteins into host cells. We mapped the interactions of bacterial and human proteins and integrated these experimental data with structural modeling and network analysis. While AlphaFold modeling identified specific functional hypotheses that are currently being followed up, network analysis revealed that effectors target host proteins linked to autoimmune and metabolic disease, and that effector abundance is specifically elevated in Crohn's disease.

Genetic variation represents another highly consequential route by which molecular networks are perturbed in disease. Translating genetic associations into mechanisms requires knowing how risk variants are organized within these networks. To link GWAS signal to mechanisms we developed a graph representation learning framework integrating network topology with multi-modal genetic evidence to predict core disease genes — validated against Mendelian disease genes and enriched for druggable, untargeted proteins and setting the foundation for further mechanistic understanding.

Achieving the virtual cell vision will require knowledge of molecular wiring in cells and understanding how that wiring is remodeled by genetic variation or hijacked by viruses and bacteria.