Decoding Cellular Systems: From Observational Atlases to Generative Interventions

Over the past decade, the field of computational cell biology has undergone a transformation — from cataloging cell types to modeling how cells behave, interact, and respond to perturbations. In this talk, Dr. Theis will review and explore how machine learning is enabling this shift, focusing on two converging frontiers: integrated cellular mapping and actionable generative models.

 

He'll begin with a brief overview of recent advances in representation learning for atlas-scale integration, highlighting work across the Human Cell Atlas and beyond. These efforts aim to unify diverse single-cell and spatial modalities into shared manifolds of cellular identity and state. As one example, he will present our recent multimodal atlas of human brain organoids, which integrates transcriptomic variation across development and lab protocols.

 

From there, he'll review the emerging landscape of foundation models in single-cell genomics, including their work on Nicheformer, a transformer trained on millions of spatial and dissociated cells. These models offer generalizable embeddings for a range of tasks—but more importantly, they set the stage for predictive modeling of biological responses.

 

He'll close by introducing perturbation models leveraging generative AI to model interventions on these systems. As example he will show Cellflow, a generative framework that learns how perturbations such as drugs, cytokines or gene edits — shift cellular phenotypes. It enables virtual experimental design, including in silico protocol screening for brain organoid differentiation. This exemplifies a move toward models that not only interpret biological systems but help shape them.