用自然语言控制模拟细胞行为，让语言直接指挥细胞聚散。
Giving Simulated Cells a Voice: Evolving Prompt-to-Intervention Models for Cellular Control
- 用大模型解析指令，生成可操控细胞的向量场。
- 在2D模拟中成功实现聚类与分散等行为响应。
- 适合想用语言控制生物系统的科研人员。
引导生物系统达到特定状态（如形态发生结果）是医学与合成生物学中的核心挑战。尽管大型语言模型（LLMs）已在人工智能系统中实现以自然语言为接口的可解释控制，但其在生物或细胞动态调控中的应用仍处于空白。本文提出一种功能完整的管道，将自然语言提示转化为空间向量场，以引导模拟的细胞集体行为。该方法结合大语言模型与可进化神经控制器（提示到干预，简称P2I），通过进化策略优化，使系统能在2D模拟环境中实现聚类、分散等行为。即使词汇受限且细胞模型简化，演化后的P2I网络仍能有效对齐细胞动态与用户以自然语言表达的目标。本研究构建了从语言输入到模拟生物电干预再到行为输出的完整闭环，为未来实现自然语言驱动的细胞控制奠定了基础。
原文摘要 · Abstract (English)
Guiding biological systems toward desired states, such as morphogenetic outcomes, remains a fundamental challenge with far-reaching implications for medicine and synthetic biology. While large language models (LLMs) have enabled natural language as an interface for interpretable control in AI systems, their use as mediators for steering biological or cellular dynamics remains largely unexplored. In this work, we present a functional pipeline that translates natural language prompts into spatial vector fields capable of directing simulated cellular collectives. Our approach combines a large language model with an evolvable neural controller (Prompt-to-Intervention, or P2I), optimized via evolutionary strategies to generate behaviors such as clustering or scattering in a simulated 2D environment. We demonstrate that even with constrained vocabulary and simplified cell models, evolved P2I networks can successfully align cellular dynamics with user-defined goals expressed in plain language. This work offers a complete loop from language input to simulated bioelectric-like intervention to behavioral output, providing a foundation for future systems capable of natural language-driven cellular control.
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