超越文本生成:大型语言模型作为编排合成生物学循环的自主代理
Anqiang Ye1, Bing-Ying Wang1, Zhenshun Cheng2
1Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China; Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University, Wuhan 430071, China.
Bioresource technology
|February 28, 2026
概括
大型语言模型 (LLM) 正在通过实现生物数据的语义分析来彻底改变合成生物学. 这些模型加速了设计-制造-测试-学习循环,为生物工程带来了新的机遇和挑战.
科学领域:
- 合成生物学 合成生物学
- 计算生物学是一种计算生物学.
- 生命科学中的人工智能
背景情况:
- 合成生物学在高维设计空间和复杂的实验优化方面面临着挑战.
- 传统的机器学习依赖于手动功能创建,限制了生物系统设计.
- 大型语言模型 (LLM) 为理解和处理复杂的生物序列数据提供了一种新的方法.
研究的目的:
- 系统地审查不同大型语言模型 (LLM) 架构在合成生物学中的应用.
- 要突出从手动特征工程到语义驱动方法的范式转变,由LLMs促进.
- 检查LLMs在促进合成生物学方面的变革性应用,挑战和未来潜力.
主要方法:
- 系统审查LLM架构及其适用于特定生物任务的适用性.
- 在序列设计,代谢工程和自动化实验室中对LLM应用进行批判性检查.
- 分析挑战,包括计算成本,可解释性和道德治理.
主要成果:
- LLM 独特适合于特定的生物任务,从序列数据中解析深层生物逻辑.
- 法律法规允许语义驱动的方法,超越传统的机器学习.
- 变革性的应用显示了生物设计和自动化前所未有的加速.
结论:
- 现在,LLM正在成为自主代理人,指挥合成生物学设计-构建-测试-学习循环.
- 人类和人工智能代理之间的协作智能将导航生物设计空间.
- 法学士准备在可持续制造,医药和其他领域推动突破.
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