合成基因线路的工程化设计研究进展与展望

doi:10.12211/2096-8280.2023-096

摘要/Abstract

摘要：

合成基因线路是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。

关键词: 合成生物学, 基因线路, 生物设计, 正交化, 标准化, 模块化, 自动化

Abstract:

Synthetic genetic circuits are engineered gene networks comprised of interacting redesigned genetic parts to perform customized functions in cells. With the rapid development of synthetic biology, synthetic genetic circuits have shown significant potential applications in many fields such as biomanufacturing, healthcare and environmental monitoring. However, the efforts to scale up genetic circuits are hindered by the limited number of orthogonal parts, the difficulty of functionally composing large-scale circuits, and the low predictability of circuit behavior. A longstanding goal of synthetic biology research is to engineer complex synthetic biological circuits, using modular genetic parts, with the same ease with which we engineer electronic circuits. Synthetic biologists have developed various genetic toolboxes and functional assembly methods over the past few decades. Here we present a current overview of the latest advances, challenges, and future prospects in genetic circuit engineering by grouping them into four facets corresponding to the four key engineering principles for circuit design, i.e. orthogonality, standardization, modularity, and automation. Firstly, the design and construction of orthogonal genetic part libraries are discussed in both prokaryotes and eukaryotes at the levels of DNA replication, transcription, and translation respectively. Standardized characterization methods and the design of modular genetic parts are subsequently summarized. Furthermore, progress in developing modular genetic circuits are presented, providing new concepts and ways for engineering increasingly large and complex circuits. Finally, how to achieve automated design and build of genetic circuits are discussed from the advances in software, hardware and artificial intelligence respectively, with an aim to replace the presently time-consuming manual trial-and-error mode and to accelerate the iterative "design-build-test-learn" cycle for improved efficiency and predictability of circuit design. The integration of these fundamental principles and the latest advances in information technology such as artificial intelligence and lab automation will accelerate the paradigm shift in genetic circuit engineering and synthetic biology research, making it feasible to design synthetic lives meeting various customized needs.

Key words: synthetic biology, genetic circuit, biodesign, orthogonality, standardization, modularity, automation

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, DOI: 10.12211/2096-8280.2023-096.

Ge GAO, Qi BIAN, Baojun WANG. Synthetic genetic circuit engineering: principles, advances and prospects[J]. Synthetic Biology Journal, DOI: 10.12211/2096-8280.2023-096.

图/表 5

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 the various steps of gene expression, including DNA storage and replication [14-20], transcription [21-45], translation [46, 47], and post-translational regulation [48-50])

表1 典型正交基因元件库的设计与表征

Table 1 Exemplar characterized 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	[21]
T7 RNAP突变体	活性变高的T7 RNAP突变体	6	6	[22]
ECF-σ因子	可替换的ECF-σ因子用于同源启动子的激活	52	20	[23]
LacI突变体	N端序列突变的LacI与突变的LacO操纵子	5	5	[26]
Cl 突变体	基于噬菌粒的定向进化	12	6	[27]
TetR同系物	元件挖掘并鉴定TetR家族类似抑制子	20	17	[28]
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	[29]
	小分子（插入基因组的小分子生物传感器）	12	12	[31]
	代谢物（代谢的多样性）	14	12	[33]
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	[34]
	突变pLux启动子序列	12	2	[35]
	对不同来源群体感应系统进行同源和非同源表征	6	3	[36]
	群感信号配体的筛选	10	6	[39]
STARs	目标RNA与小转录激活RNA	100	6	[41]
CRISPRi	高度非重复的超长sgRNA阵列	22	13	[44]
CRISPRa	修饰的sgRNA与sigma 54激活因子	5	5	[45]
核糖调控	Toehold switches	144	26	[46]
核糖调控	Toehold repressors	95	15	[47]
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	[48]
遗传密码子	筛选技术：tREX	71	23	[49]

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 architecture and exemplar web pages 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) An exemplar web page with in-depth description of the information, performance, and characterization conditions for a specific genetic part. (c) An exemplar web page (parts table) displaying a brief description and key performance data for a category of parts.

图3 合成基因线路的模块化设计（a）模块化且正交的逻辑与门和与非门控线路设计［25］；（b）模块化且放大倍数连续可调的转录信号放大线路设计［76］；（c）基于级联多层正交模块化的转录信号放大线路设计的领域目前最敏感的砷汞等重金属污染微生物传感器设计［88］；（d）同时利用四个通信通道的分布式多细胞生物计算线路设计［38］。

Fig. 3 Modular design of synthetic genetic circuits(a) Design of a modular and orthogonal genetic AND gate for the logic integration of multi-input signals [25]; (b) Design of modular and tunable genetic amplifiers for scaling transcriptional signals [76]; (c) Design of ultrasensitive bacterial sensors for arsenic and mercury by cascading multiple modular and orthogonal transcriptional amplifiers [88]; (d) Engineered biocomputing circuit that simultaneously utilizes four communication channels [38].

图4 合成基因线路的自动化“设计-构建-测试-学习”循环（其中，“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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