VCLMU: Mechanism-Centric Virtual Cell World Modeling for Perturbation Response

Predicting cellular responses to genetic perturbations is a central capability of virtual cell models and an important foundation for computational modeling of biological perturbations. Many existing perturbation models achieve accurate prediction of observed responses without explicitly representing the internal state transitions or biological mechanisms underlying them. We introduce VCLMU, a mechanism-centric world model for virtual cells. VCLMU brings the object-centric principle of world modeling to cellular systems by representing internal cellular state as a collection of Latent Mechanism Units (LMUs),which represent latent biological mechanisms corresponding to objects in an object-centric world model. Each LMU has a reusable identity and a cell-specific state. Its identity is biologically grounded in multimodal gene evidence, while its state is obtained from the current cellular observation to capture cell-specific mechanism states. Given the current cellular state and a perturbation action, the world model predicts the perturbation-induced transition of each active LMU, then aggregates the resulting mechanism states and decodes them into the transcriptional response. Built on the same LMU state representation, VCLMU supports both forward perturbation response prediction and inverse perturbation inference. The forward model predicts the perturbed cellular response from the current state and perturbation action, whereas the inverse model infers the perturbation action from the observed state transition. VCLMU is trained through two-stage pretraining. The first stage uses approximately 165K pseudo-bulk perturbation profiles to learn stable population-level responses for individual perturbation conditions and establish robust perturbation dynamics. The second stage further pretrains on large-scale, gene-aligned single-cell perturbation data to capture cell-specific heterogeneity and stochastic variation beyond the population mean. We evaluate VCLMU through forward response prediction, inverse perturbation inference, mechanistic interpretation, and ablation studies, demonstrating its ability to generalize to unseen perturbations while learning biologically structured latent dynamics.