Seminar

In this talk, we discuss the paper "Multi-Marginal Flow Matching with Adversarially Learnt Interpolants" by O. Kviman et al., 2025, arxiv. Abstract Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajectories are available, but one has only …

In this talk, we discuss the paper "Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings" by K. Rahmani et al., Nat. Comm, 2025. Abstract Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells’ electrical properties. Traditional methods like patch-clamp are precise but …

In this talk, we discuss the paper "GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design" by M. Filo et al., arxiv, 2026. Abstract Biomolecular networks underpin emerging technologies in synthetic biology—from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics—and also provide a mechanistic language for understanding complex dynamics in natural …

In this talk, we discuss the paper "TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation" by J.-B. Morlot et al., bioarxiv, 2026. Abstract Drug discovery is impeded by the difficulty of translating targets from preclinical models to patients. Here, we present TwinCell, a Large Causal Cell Model (LCCM) capable of generalising …