VCLMU：面向扰动响应的以机制为中心的虚拟细胞世界建模
VCLMU: Mechanism-Centric Virtual Cell World Modeling for Perturbation Response
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中文总结 AI 辅助
提出机制中心虚拟细胞世界模型VCLMU，以潜在机制单元表示细胞状态，将扰动作为动作，通过两阶段预训练，在多个基准上提升扰动响应预测并揭示生物响应程序。
中文摘要 AI 辅助
预测细胞对遗传扰动的响应是虚拟细胞的核心能力，也是迈向生物干预计算建模的关键一步。现有大多数模型直接将未扰动的分子谱和扰动映射到观测结果，而没有显式表示干预所诱导的潜在细胞状态转变。我们提出了一种以机制为中心的虚拟细胞世界模型，将细胞状态表示为潜在机制单元（LMU）的集合，并将遗传扰动视为对这些潜在状态的动作。每个LMU将基于多模态生物学证据的可复用身份与观测特定状态相结合，使得扰动能够在解码最终转录响应之前诱导机制特定的随机转变。我们通过两阶段预训练来训练VCLMU，首先在约20万个伪批量扰动谱上，然后在基因对齐的单细胞扰动数据上。在六个扰动不相交的基准上，VCLMU在强基线上持续提高了扰动特定响应的恢复能力，同时保持了具有竞争力的全局响应准确性。我们进一步通过扰动响应与LMU基因集之间的富集分析来剖析学到的LMU，并表明它们捕获了结构化的生物响应程序。这些结果支持机制级潜在状态转变作为旨在预测和解释细胞对生物干预响应的虚拟细胞模型的一种有用表述。
英文摘要
Predicting cellular responses to genetic perturbations is a central capability for virtual cells and a key step toward computational modeling of biological interventions. Most existing models directly map an unperturbed molecular profile and perturba- tion to the resulting observation without explicitly representing the latent cellular transition induced by the intervention. We introduce a mechanism-centric virtual cell world model that represents cellular state as a set of Latent Mechanism Units (LMUs) and treats genetic perturbations as actions on these latent states. Each LMU combines a reusable identity grounded in multimodal biological evidence with an observation-specific state, allowing a perturbation to induce mechanism- specific stochastic transitions before decoding the resulting transcriptional response. We train VCLMU through two-stage pretraining, first on around 200K pseudo-bulk perturbation profiles and then on gene-aligned single-cell perturbation data. Across six perturbation-disjoint benchmarks, VCLMU consistently improves perturbation- specific response recovery over strong baselines while maintaining competitive global response accuracy. We further analyze learned LMUs through enrichment between perturbation responses and LMU gene sets and show that they capture structured biological response programs. These results support mechanism-level latent state transition as a useful formulation for virtual cell models that aim to predict and interpret cellular responses to biological interventions.
发表机构
- Johnson & Johnson Innovative Medicine（强生创新医药）
- University of Texas at Arlington（德克萨斯大学阿灵顿分校）
机构由 AI 辅助整理，请以论文原文为准。
