CellFlow: Simulating Cellular Morphology Changes via Flow Matching

• URL: http://arxiv.org/abs/2502.09775v1
• Date: Thu, 13 Feb 2025 21:10:00 GMT
• Title: CellFlow: Simulating Cellular Morphology Changes via Flow Matching
• Authors: Yuhui Zhang, Yuchang Su, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy,
• Abstract summary: We introduce CellFlow, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations.<n>CellFlow generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes.
• Score: 44.8979602893102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlow, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlow models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as batch effects -- a major challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRx1), and combined perturbation (JUMP) datasets, CellFlow generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes, achieving a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods. Additionally, CellFlow enables continuous interpolation between cellular states, providing a potential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research.

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