AI + synthetic biology： a paradigm shift in biomanufacturing

LAN Jinggang; FU Xiongfei; WANG Xiaowo; ZHANG Xian-En

doi:10.12211/2096-8280.2026-007

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AI + synthetic biology： a paradigm shift in biomanufacturing

更新时间：2026-05-07

- AI + synthetic biology： a paradigm shift in biomanufacturing
- Synthetic Biology Journal Pages: 1-13(2026)
- 作者机构：
  
  1.深圳理工大学合成生物学院，广东深圳 518107
  2.中国科学院深圳先进技术研究院，定量合成生物学全国重点实验室，深圳合成生物学创新研究院，广东深圳 518055
  3.清华大学自动化系，合成与系统生物学研究中心，教育部生物信息学重点实验室，北京信息科学与技术国家研究中心，北京 100084
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2026-007
  CLC： Q81
- Received：25 February 2026，
  
  Revised：2026-04-21，
  
  Online First：07 May 2026，
- 稿件说明：
移动端阅览
兰晶岗，傅雄飞，汪小我，张先恩. 人工智能驱动合成生物学研究［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-007

LAN Jinggang， FU Xiongfei， WANG Xiaowo， ZHANG Xian-En. AI + synthetic biology： a paradigm shift in biomanufacturing［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-007
兰晶岗，傅雄飞，汪小我，张先恩. 人工智能驱动合成生物学研究［J］. 合成生物学， 2026， 7. DOI： 10.12211/2096-8280.2026-007 DOI：

LAN Jinggang， FU Xiongfei， WANG Xiaowo， ZHANG Xian-En. AI + synthetic biology： a paradigm shift in biomanufacturing［J］. Synthetic Biology Journal， 2026， 7. DOI： 10.12211/2096-8280.2026-007 DOI：

摘要

人工智能（AI）正在深刻重塑合成生物学的研究范式，使生命系统的设计从经验驱动迈向模型驱动。传统合成生物学依赖突变筛选与试错式优化，难以应对多尺度、高维度、强耦合的生命过程。随着组学数据的爆发式增长、自动化实验平台的普及以及深度学习技术的快速发展，AI 为揭示序列—结构—功能规律、构建可预测生物模型和实现大规模生命设计提供了全新路径。AI 合成生物学已在四个关键层级形成系统化框架：在生物大分子层面，蛋白语言模型与生成式结构模型使从头设计酶、受体与自组装材料成为可能；在基因组层面，深度学习推动突变机制建模、大片段序列生成与谱系动力学推断，为可编程基因组构建奠定基础；在细胞层面，AI 与机理模型结合加速虚拟细胞构建，使细胞行为实现可量化预测；在平台层面，多智能体与自动化“设计-建造-测试-学习”（DBTL）循环支持路径规划、酶功能预测与实验调度的全流程自动化。总体而言，AI 使合成生物学从局部优化走向系统生成，从经验探索走向预测设计，为生命系统的可控重构和生物制造创新提供了核心动力。

Abstract

Artificial intelligence (AI) is profoundly reshaping the research paradigm of synthetic biology

shifting the design of living systems from empirically driven approaches to model-driven ones. Traditional synthetic biology relies on mutagenesis screening and trial-and-error optimization

making it difficult to address multiscale

high-dimensional

and strongly coupled biological processes. With the explosive growth of omics data

the widespread adoption of automated experimental platforms

and the rapid development of deep learning technologies

AI provides a new pathway to uncover sequence–structure–function relationships

build predictive biological models

and enable large-scale design of living systems. AI-driven synthetic biology has established a systematic framework across four key levels: at the biomacromolecular level

protein language models and generative structural models make de novo design of enzymes

receptors

and self-assembling materials possible; at the genomic level

deep learning advances modeling of mutational mechanisms

large-fragment sequence generation

and inference of phylogenetic dynamics

laying the foundation for programmable genome construction; at the cellular level

the integration of AI with mechanistic models accelerates virtual cell development

enabling quantitatively predictive descriptions of cellular behavior; at the platform level

multi-agent systems and automated "design–build–test–learn" (DBTL) cycles support end-to-end automation of pathway planning

enzyme function prediction

and experimental scheduling. Overall

AI is moving synthetic biology from local optimization to system-level generation

and from empirical exploration to predictive design

providing a core driving force for the controllable reprogramming of living systems and innovation in biomanufacturing.

关键词

Keywords

references

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