合成生物学 ›› 2025, Vol. 6 ›› Issue (1): 45-64.DOI: 10.12211/2096-8280.2023-096
高歌1,2, 边旗1,2, 王宝俊1,2
收稿日期:
2023-12-01
修回日期:
2024-04-10
出版日期:
2025-01-31
发布日期:
2025-03-12
通讯作者:
王宝俊
作者简介:
基金资助:
Ge GAO1,2, Qi BIAN1,2, Baojun WANG1,2
Received:
2023-12-01
Revised:
2024-04-10
Online:
2025-01-31
Published:
2025-03-12
Contact:
Baojun WANG
摘要:
合成基因线路利用合成生物学的技术和方法,将生物元件进行重新设计与构建,使人工设计的生物分子线路在活细胞中行使特定生物功能,在生物制造、医疗健康以及环境监测等领域具有巨大的潜力。但其工程化设计仍受到各种因素的制约,包括正交元器件数量有限、大规模线路组装困难、线路行为预测性低等。根据研究者们开发的各种调控元件工具箱和组装方法,本文逐点阐述了工程化设计基因线路所需遵循的几个核心原则:正交化、标准化、模块化与自动化。文章从DNA复制、转录和翻译层面介绍了正交基因元件库的构建和改造方法;全面总结了基因元件的标准化定量表征方法与标准元件设计方法;并介绍了本团队与其他团队在模块化基因线路设计方面的相关进展;分别从软件、硬件和人工智能角度展示如何实现基因线路的自动化设计。最后,本文探讨了基因线路设计的未来发展趋势,指出需要进一步融合人工智能和自动化等信息技术来加速基因线路“设计-构建-测试-学习”循环的迭代,提高线路设计的功能可预测性和复杂性,高效设计出符合目标需求的人造生命体。
中图分类号:
高歌, 边旗, 王宝俊. 合成基因线路的工程化设计研究进展与展望[J]. 合成生物学, 2025, 6(1): 45-64.
Ge GAO, Qi BIAN, Baojun WANG. Synthetic genetic circuit engineering: principles, advances and prospects[J]. Synthetic Biology Journal, 2025, 6(1): 45-64.
图1 合成基因线路中已经验证的用于基因表达控制的正交元件与调控工具(合成基因元件可以在遗传信息表达的不同过程中发挥调控作用,包括DNA存储与复制[14-20]、转录[21-45]、翻译[46-47]以及翻译后调控[48-50])
Fig. 1 Validated orthogonal parts and tools for precise gene expression control in genetic circuit design(Synthetic genetic parts can regulate various steps of gene expression, including DNA storage and replication[14-20], transcription[21-45], translation[46-47], and post-translational regulation[48-50].)
元件名称 | 获得正交元件的方法 | 元件数量 | 正交元件数量 | 参考文献 |
---|---|---|---|---|
T7 RNAP突变体 | 毒性降低的T7 RNAP突变体 | 4 | 4 | [ |
活性变高的T7 RNAP突变体 | 6 | 6 | [ | |
ECF-σ因子 | 可替换的ECF-σ因子用于同源启动子的激活 | 52 | 20 | [ |
LacI突变体 | N端序列突变的LacI与突变的LacO操纵子 | 5 | 5 | [ |
Cl 突变体 | 基于噬菌粒的定向进化 | 12 | 6 | [ |
TetR同系物 | 元件挖掘并鉴定TetR家族类似抑制子 | 20 | 17 | [ |
可诱导表达系统 | 金属离子(由金属离子诱导的调控因子和相应的启动子) | 5 | 5 | [ |
小分子(插入基因组的小分子生物传感器) | 12 | 12 | [ | |
代谢物(代谢的多样性) | 14 | 12 | [ | |
群体感应 | 对信号、遗传串扰优化后的群感调控因子和启动子 | 4 | 2 | [ |
突变pLux启动子序列 | 12 | 2 | [ | |
对不同来源群体感应系统进行同源和非同源表征 | 6 | 3 | [ | |
群感信号配体的筛选 | 10 | 6 | [ | |
STARs | 目标RNA与小转录激活RNA | 100 | 6 | [ |
CRISPRi | 高度非重复的超长sgRNA阵列 | 22 | 13 | [ |
CRISPRa | 修饰的sgRNA与sigma 54激活因子 | 5 | 5 | [ |
核糖调控 | Toehold switches | 144 | 26 | [ |
Toehold repressors | 95 | 15 | [ | |
断裂内含肽 | 元件挖掘并测试不同的断裂内含肽交叉活性 | 34 | 15 | [ |
遗传密码子 | 筛选技术:tREX | 71 | 23 | [ |
表1 典型正交基因元件库的设计与表征
Table 1 Design and characterization of the libraries of orthogonal genetic parts
元件名称 | 获得正交元件的方法 | 元件数量 | 正交元件数量 | 参考文献 |
---|---|---|---|---|
T7 RNAP突变体 | 毒性降低的T7 RNAP突变体 | 4 | 4 | [ |
活性变高的T7 RNAP突变体 | 6 | 6 | [ | |
ECF-σ因子 | 可替换的ECF-σ因子用于同源启动子的激活 | 52 | 20 | [ |
LacI突变体 | N端序列突变的LacI与突变的LacO操纵子 | 5 | 5 | [ |
Cl 突变体 | 基于噬菌粒的定向进化 | 12 | 6 | [ |
TetR同系物 | 元件挖掘并鉴定TetR家族类似抑制子 | 20 | 17 | [ |
可诱导表达系统 | 金属离子(由金属离子诱导的调控因子和相应的启动子) | 5 | 5 | [ |
小分子(插入基因组的小分子生物传感器) | 12 | 12 | [ | |
代谢物(代谢的多样性) | 14 | 12 | [ | |
群体感应 | 对信号、遗传串扰优化后的群感调控因子和启动子 | 4 | 2 | [ |
突变pLux启动子序列 | 12 | 2 | [ | |
对不同来源群体感应系统进行同源和非同源表征 | 6 | 3 | [ | |
群感信号配体的筛选 | 10 | 6 | [ | |
STARs | 目标RNA与小转录激活RNA | 100 | 6 | [ |
CRISPRi | 高度非重复的超长sgRNA阵列 | 22 | 13 | [ |
CRISPRa | 修饰的sgRNA与sigma 54激活因子 | 5 | 5 | [ |
核糖调控 | Toehold switches | 144 | 26 | [ |
Toehold repressors | 95 | 15 | [ | |
断裂内含肽 | 元件挖掘并测试不同的断裂内含肽交叉活性 | 34 | 15 | [ |
遗传密码子 | 筛选技术:tREX | 71 | 23 | [ |
图2 基因元器件精选数据库BioPartsDB的网站结构与内容设计示意图[73](a) BioPartsDB数据库的设计架构图,箭头代表数据库里的预期用户流,箭头往下越来越多的堆叠面板代表该部分的页面数量越多,包含的信息越详细;(b)具体到某个基因元件数据的页面内容示例;(c)某种类型的基因元件列表页面内容示例,显示元件的简要说明和关键性能数据信息
Fig. 2 Design portfolio and the web architecture of the BioPartsDB platform[73](a) A simplified diagram showing the information flow of the database platform. Arrows indicate the intended user browsing along the platform's webpages. Increasingly stacked panels indicate the higher number of pages in each section and consequently the more detailed level of information. (b) A web page with in-depth description of the information, performance, and characterization conditions for a specific genetic part. (c) A table for parts with a brief description and data of their key performance.
图4 合成基因线路的自动化“设计-构建-测试-学习”循环(“构建与测试”的生物铸造厂设施摘自文献[97],“学习”的模型改编自文献[98])
Fig. 4 An automated "design-build-test-learn" cycle for genetic circuit engineering.(Automated instrumentation in biofoundries is adapted from reference [97]. The neural network-based deep learning model is adapted from reference [98].)
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