用大模型自动发现生物系统微分方程，从数据到机制建模一步到位。
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
- 基于大模型与符号回归的智能体框架，自动推导生物系统微分方程。
- 在无数据和有数据条件下均实现正确状态变量识别与结构恢复。
- 适合系统生物学、计算建模研究者，推动自动化科学发现。
自动科学发现一直是计算学者的追求目标——让机器自主揭示自然规律，使计算系统超越数据拟合工具，转向宇宙机制模型的生成与优化。近年来，符号回归（SR）和大语言模型（LLM）驱动的智能体进展表明，这类系统可从数据中恢复方程，融入领域先验，并自动化部分研究流程。然而，现有方法多聚焦于狭窄的方程发现基准或广义端到端自动化流程，而生物系统仍相对未被充分探索。本文提出MEDA系统，一个由大语言模型与符号回归驱动的智能体框架，用于发现生物及类生物动力学系统的常微分方程（ODE）模型。MEDA能检索背景知识、定义可接受变量、生成机制约束、提出候选方程并进行拟合与评估。我们在经典模型检索、基于推理的未见变体外推，以及开放发现任务中评估了该系统，涵盖有/无实验数据场景。结果表明，MEDA成功识别出正确状态变量，在检索与外推任务中实现强结构恢复，并生成具有生物学合理性的发现导向模型。消融与鲁棒性分析显示，知识引导的形式化与机制约束是核心组件；仅靠数值拟合虽可保持轨迹兼容，但可能生成生物学错误的方程。
原文摘要 · Abstract (English)
Automatic scientific discovery has long been a goal of computational scholars - a machine that can discover nature's secrets on its own, moving computational systems beyond data-fitting tools toward the generation and refinement of mechanistic models of the universe. Recent advances in symbolic regression (SR) and large-language-model (LLM)-based agents suggest that such systems can recover equations from data, incorporate domain priors, and automate parts of the research workflow. However, most existing approaches either focus on narrow equation-discovery benchmarks or broad end-to-end automation pipelines, while biological systems remain comparatively underexplored. Here, we introduce the MEDA system, an LLM- and SR-powered agentic framework for discovering ordinary-differential-equation (ODE) models of biological and biologically inspired dynamical systems. MEDA retrieves background knowledge, defines admissible variables, generates mechanistic constraints, proposes candidate ODEs, and fits and evaluates them. We evaluate it across canonical model retrieval, reasoning-based extrapolation to unseen variants, and open-ended discovery, with and without experimental data. Across these settings, MEDA recovered the correct state variables, achieved strong structural recovery in retrieval and extrapolation tasks, and produced biologically plausible discovery-oriented models. Ablation and robustness analyses show that knowledge-guided formalization and mechanistic constraints are load-bearing components, whereas numerical fitting alone can preserve trajectory-compatible but biologically incorrect equations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。