综述：虚拟细胞从机制建模到数据驱动整合的进展与挑战
Virtual cells: progress and challenges from mechanistic modeling to data-driven integration
这篇发表于 Briefings in Bioinformatics 的综述梳理了虚拟细胞研究的进展与挑战。虚拟细胞旨在用计算方式表示细胞状态，并预测细胞对遗传、化学、环境、时间和空间条件的响应。作者用状态编码（E）、状态转换（T）和观测解码（D）作为统一的概念框架，覆盖显式机制模型、数据驱动的基础模型与扰动模型、多模态系统以及空间与多尺度模拟。文章从模型边界、数据基础、资源披露、分布外评测、因果证据和实验验证等方面展开讨论，并指出独立基准显示参数数量和训练集规模并不会自动带来稳健的分布外泛化，仅靠转录组表征也无法完整刻画细胞的功能状态。
来源：Briefings in Bioinformatics · doi.org
综述用编码、转换、解码三层框架梳理虚拟细胞模型，并指出参数规模与训练集大小未必带来稳健的分布外泛化。
Virtual cells aim to represent cellular states computationally and predict responses to genetic, chemical, environmental, temporal, and spatial conditions. For comparative purposes, this review uses state encoding (E), state transition (T), and observation decoding (D) as a conceptual abstraction spanning explicit mechanistic models, data-driven foundation and perturbation models, multimodal systems, and spatial and multiscale simulation. We examine model boundaries, data foundations, resource disclosure, out-of-distribution evaluation, causal evidence, and experimental validation. Independent benchmarks show that parameter count and training-set size do not automatically yield robust out-of-distribution generalization, while transcriptomic representations alone do not capture the full functional state of a cell. Trustworthy virtual cells will require perturbation-responsive transitions, cross-modal alignment, continuous-time dynamics, mechanistic constraints, calibrated uncertainty, independent experimental validation, and, where appropriate, closed-loop experimental feedback.