发表机构
University of Nebraska-Lincoln（内布拉斯加大学林肯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在降低基于约束的代谢建模使用门槛，开发了MechAInistic多智能体系统，借助大语言模型，围绕特定模式将自然语言问题转化为可执行工作流程，通过两个免疫细胞代谢模型用例验证，能生成可追溯的治疗假设。
AI 中文摘要
基于约束的代谢建模是研究细胞状态和疾病机制基础的有力方法，但有效使用它需要大量计算专业知识和多步骤分析的精心协调。我们开发了MechAInistic来降低这一障碍，使研究人员能够用自然语言提出复杂的生物学问题。利用大语言模型，MechAInistic是一个围绕架构师-评审员模式组织的多智能体系统，它将自然语言问题转化为可执行的、基于模型的工作流程，并生成结构化报告。该系统支持多种任务，包括途径比较、扰动分析、药物靶点探索等。我们使用两个配对的免疫细胞代谢模型用例开发并评估了MechAInistic以生成治疗假设。在类风湿性关节炎（RA）的幼稚B细胞与健康对照的配对研究中，MechAInistic识别出线粒体代谢重排，并提名Devimistat/CPI-613作为以OGDH为中心的研究假设。在多发性硬化症（MS）和健康对照的配对CD4+ Th17细胞研究中，相同的工作流程确定NADP依赖的异柠檬酸脱氢酶为最佳单一靶点，并提出ivosidenib作为FDA批准的重新利用候选药物。这些结果表明，MechAInistic将自然语言生物学问题转化为可执行的、基于模型的工作流程，用于可追溯的治疗假设生成。
英文摘要
Constraint-based metabolic modeling is a powerful way to study the mechanistic basis of cellular states and disease, but its effective use demands substantial computational expertise and careful coordination of multi-step analyses. We developed MechAInistic to lower this barrier and enable researchers to ask complex biological questions in natural language. Harnessing large language models, MechAInistic is a multi-agent system organized around an Architect-Reviewer pattern that transforms a natural-language question into an executable, model-grounded workflow and generates a structured report. The system supports a variety of tasks, including pathway comparison, perturbation analysis, drug-target exploration, and literature-grounded interpretation across paired metabolic model states. We developed and evaluated MechAInistic using two paired immune-cell metabolic-model use cases for therapeutic hypothesis generation. For Naive B cells from rheumatoid arthritis (RA) paired with healthy controls, MechAInistic identified mitochondrial metabolic rewiring and nominated Devimistat/CPI-613 as an investigational OGDH-centered hypothesis. In a paired CD4+ Th17 cell study from multiple sclerosis (MS) and healthy controls, the same workflow identified NADP-dependent isocitrate dehydrogenase as the optimal single target and proposed ivosidenib as an FDA-approved repurposing candidate. Together, these results show that MechAInistic converts natural-language biological questions into executable, model-grounded workflows for traceable therapeutic hypothesis generation.
Comments23 pages, 6 figures,
