Jiaqi Zhang: Learning causal cellular programs from large-scale perturbations

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Tuesday, Sep 23, 2025: Young researchers' seminar
Speaker 1: Jiaqi Zhang (MIT)
Title: Learning causal cellular programs from large-scale perturbations
Abstract: Complex molecular mechanisms govern cellular functions in living organisms and shape their behavior in health and disease. Understanding these mechanisms can greatly accelerate therapeutic discovery, yet it remains challenging due to the high dimensionality and intricate dependencies within biological systems. Recent advances in experimental technologies, however, are beginning to make this problem more tractable. For example, we can now systematically perturb individual or combinations of genes in single cells and measure their downstream effects, enabling empirical identification and validation of causal relationships. Nevertheless, perturbation data remain noisy and high-dimensional, with effects often sparse and subtle.
In this talk, I will present our work addressing two key challenges in this emerging paradigm: (1) how to define and learn the causal programs that govern high-dimensional or perceptual data; (2) how to model perturbational effects in a way that enables the prediction of novel perturbations. For (1), we introduce new causal representation theories that guarantee identifiability of the underlying causal programs, given sufficient samples and regularizing condition. For (2), we develop a modular framework that models perturbation effects through distributional discrepancies. This approach captures nuanced, sample-level changes and enables extrapolation to predict the effects of unseen perturbations across diverse conditions and data types.