Rewire then Express: Cascaded Flow Matching for Single-Cell Perturbation Response Prediction

Predicting cellular responses to genetic and chemical perturbations is a central challenge in functional genomics and drug discovery. Many recent models formulate this task as a single-stage transport from unperturbed to perturbed states. Inspired by the role of upstream regulatory changes in shaping transcriptional responses, we introduce *Rewire-then-Express*, a generative paradigm that structures perturbation prediction as an ordered cascade. The model first predicts an intermediate response representation, supervised by perturbation-induced shifts in pretrained single-cell embeddings, and then generates transcriptomic profiles conditioned on this representation. We instantiate this paradigm as **scCasFlow**, a Cascaded Flow Matching framework. Dual temporal coordinates separately parameterize intermediate-response and transcriptomic generation, while an ordered scheduling strategy coordinates their progression according to the intermediate-to-expression ordering. Flow matching operates in latent space, with a lightweight decoder enabling prediction across the retained gene vocabulary without highly variable gene selection. On the Norman and ComboSciPlex benchmarks, scCasFlow improves predictive performance over the evaluated methods under held-out perturbation splits and achieves more than 100× inference throughput of the evaluated expression-space flow matching baseline. Post hoc analyses further identify associations between the intermediate representations and transcription-factor activity scores inferred from held-out expression profiles, alongside agreement between predicted and observed pathway-aggregated expression changes. Together, these results support the predictive value of an explicitly supervised, ordered generative factorization for cellular perturbation modeling. Code is available at [https://anonymous.4open.science/r/scCasFlow-80E8](https://anonymous.4open.science/r/scCasFlow-80E8).