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.
@article{arxiv.2510.07706,
title = {Large Language Models Meet Virtual Cell: A Survey},
author = {Krinos Li and Xianglu Xiao and Shenglong Deng and Lucas He and Zijun Zhong and Yuanjie Zou and Zhonghao Zhan and Zheng Hui and Weiye Bao and Guang Yang},
journal= {arXiv preprint arXiv:2510.07706},
year = {2025}
}