Mini Brains, Beating Heart Organoids, and the AI That Predicts Them — Fabian Theis, Helmholtz Munich
Fabian Theis, Director of the Computational Health Center at Helmholtz Munich, joins co-hosts RJ Honicky and Brandon Anderson to discuss the evolution of virtual cells, single-cell transcriptomics, and perturbation biology. They explore how large-scale multiscale biological datasets, foundation models like scConcept and Nicheformer, and generative modeling techniques like CellFlow are enabling predictive cellular world models. The conversation also examines the challenges of baseline models, closed-loop active learning with LabCompass, the European biotech startup ecosystem, and interdisciplinary graduate training frameworks.
- Hardcoded biological assumptions hurt model performance once data expands. Theis points out that decades of machine learning show rigid a priori constraints consistently lose predictive power to flexible architectures. Read →
- Software-only biology platforms struggle to survive because venture investors eventually force them to pivot into internal drug discovery pipelines to prove financial returns. Read →
- Simple linear models and mean predictors routinely beat complex neural networks on noisy single-cell biology benchmarks. Read →
- A virtual cell functions as an input-output predictor rather than an observational atlas. Read →