# CellFlux: Simulating Cellular Morphology Changes via Flow Matching

arXiv: https://arxiv.org/abs/2502.09775 · HTML: https://mlsanity.com/paper/2502.09775/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 15 of 15)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [VideoPoet: A Large Language Model for Zero-Shot Video Generation](https://mlsanity.com/paper/2312.14125/) (2023)
- [Flow Matching Guide and Code](https://mlsanity.com/paper/2412.06264/) (2024)
- [Guided Flows for Generative Modeling and Decision Making](https://mlsanity.com/paper/2311.13443/) (2023)
- [How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities](https://mlsanity.com/paper/2409.11654/) (2024)
- [RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods](https://mlsanity.com/paper/2301.05768/) (2023)
- [PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images](https://mlsanity.com/paper/2312.08290/) (2023)

## Built on by (most cited first, 20 of 20)

- [Flow Matching Meets Biology and Life Science: A Survey](https://mlsanity.com/paper/2507.17731/) (2025)
- [Branched Schr"odinger Bridge Matching](https://mlsanity.com/paper/2506.09007/) (2025)
- [MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging](https://mlsanity.com/paper/2510.01298/) (2025)
- [ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data](https://mlsanity.com/paper/2510.02952/) (2025)
- [CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning](https://mlsanity.com/paper/2603.21743/) (2026)
- [Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling](https://mlsanity.com/paper/2510.06634/) (2025)
- [Compressing Biology: Evaluating the Stable Diffusion VAE for Phenotypic Drug Discovery](https://mlsanity.com/paper/2510.19887/) (2025)
- [Entangled Schr"odinger Bridge Matching](https://mlsanity.com/paper/2511.07406/) (2025)
- [Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform](https://mlsanity.com/paper/2512.08478/) (2025)
- [Shortest-Path Flow Matching with Mixture-Conditioned Bases for OOD Generalization to Unseen Conditions](https://mlsanity.com/paper/2601.11827/) (2026)
- [Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging](https://mlsanity.com/paper/2603.21717/) (2026)
- [Elucidating the Design Space of Flow Matching for Cellular Microscopy](https://mlsanity.com/paper/2603.26790/) (2026)
- [SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes](https://mlsanity.com/paper/2507.04704/) (2025)
- [Artificial Intelligence Virtual Cells: From Measurements to Decisions across Modality, Scale, Dynamics, and Evaluation](https://mlsanity.com/paper/2510.12498/) (2025)
- [Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics](https://mlsanity.com/paper/2604.22832/) (2026)
- [FreeBridge: Variational Schr"odinger Bridges for Cellular Transition Dynamics](https://mlsanity.com/paper/2606.11286/) (2026)
- [PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators](https://mlsanity.com/paper/2606.27752/) (2026)
- [Conditional Neural Optimal Transport for Predicting Cellular Phenotypes from Molecular Structure](https://mlsanity.com/paper/2608.14293/) (2026)
- [Joint Flow Matching Enables Continuous Dose-Conditioned Cell Morphing](https://mlsanity.com/paper/2608.16424/) (2026)
- [PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation](https://mlsanity.com/paper/2610.17066/) (2026)

## Abstract (arXiv)

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/.