BaiZe: A Multi-View Dynamic Framework for Simulating and Interpreting Cellular Responses Across Perturbation Contexts

Accurately predicting how cells respond to perturbations is important for understanding cellular regulation and prioritizing experimental interventions, yet existing models are often designed for specific perturbation types or biological contexts. Here we present BaiZe, a multi-view conditional state-transition framework that predicts the post-perturbation transcriptome from a control-state transcriptome together with genetic, chemical, temporal and optional chromatin-accessibility information. BaiZe models perturbation responses as context-dependent transitions between cellular states. BaiZe supports prediction across held-out cell states and genetic perturbations, unseen multi-gene combinations, chemical structures and doses, temporal stages and species contexts. Benchmarking across diverse perturbation settings demonstrates that BaiZe effectively recovers major transcriptional response programs under previously unseen conditions. Incorporating matched ATAC-seq context further improves selected state-transition predictions and enables model-based attribution of chromatin regions to response-associated genes and pathways. BaiZe also supports few-shot transfer of perturbation responses from human to mouse cellular systems and connects predicted transcriptomes to candidate morphology projections. To facilitate interpretation and use, BaiZe-Agent organizes response genes, pathways, chromatin evidence, cross-species predictions and projected phenotypes into traceable, queryable perturbation records. Together, BaiZe provides a broadly applicable framework for predicting and interpreting context-dependent cellular responses and for prioritizing hypotheses across diverse perturbation settings.