Studying biological systems is hard, since they are the domain of microscopic processes that are typically hard to measure and observe and mired in complexity. A typical approach towards studying systems of such complexity is to perform perturbations, study their outcomes, and try to understand the links to mechanisms we may want to control better. In this talk, we will talk about a class of deep generative models [1] that is tailored to this task, in that it studies readouts of cells and disentangles latent spaces suitably to isolate perturbation effects. We will introduce the model, how it can help us perform counterfactual reasoning over cells, discuss evaluation of such models, and sketch the work ahead to apply it fruitfully in service of discovery work.

[1] Modelling cellular perturbations with the sparse additive mechanism shift variational autoencoder, Michael Bereket, Theofanis Karaletsos, NeurIPS2023