用流匹配模拟细胞形态变化，区分真实扰动与实验误差。
CellFlux: Simulating Cellular Morphology Changes via Flow Matching
- 通过流匹配建模细胞从正常到扰动状态的分布变化。
- 在三个数据集上提升FID分数35%，作用机制预测准确率提高12%。
- 支持细胞状态连续插值，适合研究药物或基因扰动动态。
构建能精确模拟细胞行为的虚拟细胞一直是计算生物学的愿景。我们提出CellFlux，一种基于流匹配的图像生成模型，用于模拟化学和遗传扰动引起的细胞形态变化。与以往方法不同，CellFlux建模从无扰动到扰动状态的整体分布转变，有效区分真实扰动效应与实验中的批次效应等伪影——这是生物数据中的主要挑战。在化学（BBBC021）、遗传（RxRx1）及联合扰动（JUMP）数据集上评估，CellFlux生成的细胞图像具有生物学意义，准确捕捉了扰动特异的形态变化，在FID评分上比现有方法提升35%，作用机制预测准确率提高12%。此外，该模型支持细胞状态间的连续插值，为研究扰动动力学提供潜在工具。这些能力标志着向实现虚拟细胞建模迈出重要一步。
原文摘要 · Abstract (English)
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/.
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