CellFlux: Simulating Cellular Morphology Changes via Flow Matching

Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux 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, CellFlux 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, CellFlux 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. Project page: https: //yuhui-zh15. github. io/CellFlux/.

Authors

• Yuhui Zhang
• Yuchang Su
• Chenyu Wang 0003
• Tianhong Li
• Zoe Wefers
• Jeffrey J. Nirschl
• James Burgess
• Daisy Ding
• Alejandro Lozano
• Emma Lundberg
• Serena Yeung

Keywords

• flow matching
• cell image
• drug discovery
• generative models