用大模型构建虚拟细胞，模拟和预测细胞行为。
Large Language Models Meet Virtual Cell: A Survey
- 将大模型当作智能顾问或执行者，驱动细胞建模
- 实现细胞状态表示、扰动预测与基因调控推断
- 适合生物信息学与系统生物学研究者参考
大型语言模型（LLMs）正推动细胞生物学变革，助力构建“虚拟细胞”——可表征、预测并推理细胞状态与行为的计算系统。本文全面综述了面向虚拟细胞建模的LLM技术。提出统一分类体系，将现有方法分为两类范式：作为‘预言家’的LLMs，用于直接细胞建模；作为‘代理’的LLMs，用于协调复杂科学任务。识别出三大核心任务：细胞表征、扰动预测与基因调控推断，并系统回顾相关模型、数据集、评估基准，以及可扩展性、泛化性和可解释性等关键挑战。
原文摘要 · Abstract (English)
Large language models (LLMs) are transforming cellular biology by enabling the development of "virtual cells"--computational systems that represent, predict, and reason about cellular states and behaviors. This work provides a comprehensive review of LLMs for virtual cell modeling. We propose a unified taxonomy that organizes existing methods into two paradigms: LLMs as Oracles, for direct cellular modeling, and LLMs as Agents, for orchestrating complex scientific tasks. We identify three core tasks--cellular representation, perturbation prediction, and gene regulation inference--and review their associated models, datasets, evaluation benchmarks, as well as the critical challenges in scalability, generalizability, and interpretability.
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