CellFlux: Simulating Cellular Morphology Changes via Flow Matching
This work addresses the challenge of accurately modeling cellular behaviors in computational biology, enabling better virtual cell simulations for biomedical research, though it appears incremental as it builds on flow matching techniques.
The paper tackles the problem of simulating cellular morphology changes due to chemical and genetic perturbations by introducing CellFlux, a flow matching-based image-generative model that achieves a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods.
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/.