DynamicVC: A Trajectory-Ground Biological World Model For Virtual Cell Simulation

Building predictive virtual cells promises to uncover the mechanisms governing cellular behavior. Existing language model-based virtual cell systems rely largely on retrieved knowledge, while continuous generative models are typically evaluated only through their endpoint predictions. We introduce DynamicVC, a framework that converts conditional flow matching trajectories into structured evidence for perturbation reasoning. DynamicVC summarizes control-relative expression changes, vector field patterns along sampled ODE trajectories, endpoint effects, and directional agreement across stochastic initializations in a SimContext (Dynamic Simulation Context). It combines model-derived evidence with retrieved biological context to predict whether a drug induces differential expression of a target gene and to generate an evidence-grounded explanation.We evaluate DynamicVC on 55,400 queries involving held-out drugs across five cell lines. It achieves 79.61% AUROC and 64.42% Macro F1, improving over the strongest baseline for each metric by 12.82 pp and 11.06 pp, respectively. Comparisons across reasoning backbones and numerical evidence sources further show that context-specific response predictions consistently improve language model-based perturbation reasoning. DynamicVC is available at https://anonymous.4open.science/r/DynamicVC-50BF.