Synthetic genetic circuit engineering: principles, advances and prospects

doi:10.12211/2096-8280.2023-096

Abstract

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

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

CLC Number:

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.

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.

Figures/Tables 5

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])

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]

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.

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].

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].)

References 122

1	MENG F K, ELLIS T. The second decade of synthetic biology: 2010-2020[J]. Nature Communications, 2020, 11(1): 5174.
2	WANG X Y, ZHOU N, WANG B J. Bacterial synthetic biology: tools for novel drug discovery[J]. Expert Opinion on Drug Discovery, 2023, 18(10): 1087-1097.
3	TAMSIR A, TABOR J J, VOIGT C A. Robust multicellular computing using genetically encoded NOR gates and chemical 'wires'[J]. Nature, 2011, 469(7329): 212-215.
4	ELOWITZ M B, LEIBLER S. A synthetic oscillatory network of transcriptional regulators[J]. Nature, 2000, 403(6767): 335-338.
5	GARDNER T S, CANTOR C R, COLLINS J J. Construction of a genetic toggle switch in Escherichia coli [J]. Nature, 2000, 403(6767): 339-342.
6	ROQUET N, SOLEIMANY A P, FERRIS A C, et al. Synthetic recombinase-based state machines in living cells[J]. Science, 2016, 353(6297): aad8559.
7	RUBENS J R, SELVAGGIO G, LU T K. Synthetic mixed-signal computation in living cells[J]. Nature Communications, 2016, 7: 11658.
8	BRADLEY R W, BUCK M, WANG B J. Tools and principles for microbial gene circuit engineering[J]. Journal of Molecular Biology, 2016, 428(5 Pt B): 862-888.
9	GAO Y L, WANG L, WANG B J. Customizing cellular signal processing by synthetic multi-level regulatory circuits[J]. Nature Communications, 2023, 14(1): 8415.
10	XIANG Y Y, DALCHAU N, WANG B J. Scaling up genetic circuit design for cellular computing: advances and prospects[J]. Natural Computing, 2018, 17(4): 833-853.
11	ŞIMŞEK E, YAO Y, LEE D, et al. Toward predictive engineering of gene circuits[J]. Trends in Biotechnology, 2023, 41(6): 760-768.
12	PECCOUD J, BLAUVELT M F, CAI Y Z, et al. Targeted development of registries of biological parts[J]. PLoS One, 2008, 3(7): e2671.
13	COSTELLO A, BADRAN A H. Synthetic biological circuits within an orthogonal central dogma[J]. Trends in Biotechnology, 2021, 39(1): 59-71.
14	PARK M, PATEL N, KEUNG A J, et al. Engineering epigenetic regulation using synthetic read-write modules[J]. Cell, 2019, 176(1-2): 227-238.e20.
15	DHAMI K, MALYSHEV D A, ORDOUKHANIAN P, et al. Systematic exploration of a class of hydrophobic unnatural base pairs yields multiple new candidates for the expansion of the genetic alphabet[J]. Nucleic Acids Research, 2014, 42(16): 10235-10244.
16	HOSHIKA S, LEAL N A, KIM M J, et al. Hachimoji DNA and RNA: a genetic system with eight building blocks[J]. Science, 2019, 363(6429): 884-887.
17	RAVIKUMAR A, ARZUMANYAN G A, OBADI M K A, et al. Scalable, continuous evolution of genes at mutation rates above genomic error thresholds[J]. Cell, 2018, 175(7): 1946-1957.e13.
18	ZHONG Z W, RAVIKUMAR A, LIU C C. Tunable expression systems for orthogonal DNA replication[J]. ACS Synthetic Biology, 2018, 7(12): 2930-2934.
19	TIAN R Z, ZHAO R Z, GUO H Y, et al. Engineered bacterial orthogonal DNA replication system for continuous evolution[J]. Nature Chemical Biology, 2023, 19(12): 1504-1512.
20	YANG L, NIELSEN A A, FERNANDEZ-RODRIGUEZ J, et al. Permanent genetic memory with >1-byte capacity[J]. Nature Methods, 2014, 11(12): 1261-1266.
21	TEMME K, HILL R, SEGALL-SHAPIRO T H, et al. Modular control of multiple pathways using engineered orthogonal T7 polymerases[J]. Nucleic Acids Research, 2012, 40(17): 8773-8781.
22	MEYER A J, ELLEFSON J W, ELLINGTON A D. Directed evolution of a panel of orthogonal T7 RNA polymerase variants for in vivo or in vitro synthetic circuitry[J]. ACS Synthetic Biology, 2015, 4(10): 1070-1076.
23	RHODIUS V A, SEGALL-SHAPIRO T H, SHARON B D, et al. Design of orthogonal genetic switches based on a crosstalk map of σs, anti-σs, and promoters[J]. Molecular Systems Biology, 2013, 9: 702.
24	BERVOETS I, VAN BREMPT M, VAN NEROM K, et al. A sigma factor toolbox for orthogonal gene expression in Escherichia coli [J]. Nucleic Acids Research, 2018, 46(4): 2133-2144.
25	WANG B J, KITNEY R I, JOLY N, et al. Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology[J]. Nature Communications, 2011, 2: 508.
26	ZHAN J, DING B, MA R, et al. Develop reusable and combinable designs for transcriptional logic gates[J]. Molecular Systems Biology, 2010, 6: 388.
27	BRÖDEL A K, JARAMILLO A, ISALAN M. Engineering orthogonal dual transcription factors for multi-input synthetic promoters[J]. Nature Communications, 2016, 7: 13858.
28	STANTON B C, NIELSEN A A, TAMSIR A, et al. Genomic mining of prokaryotic repressors for orthogonal logic gates[J]. Nature Chemical Biology, 2014, 10(2): 99-105.
29	WANG B J, BARAHONA M, BUCK M. A modular cell-based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals[J]. Biosensors & Bioelectronics, 2013, 40(1): 368-376.
30	LOPRESIDE A, WAN X Y, MICHELINI E, et al. Comprehensive profiling of diverse genetic reporters with application to whole-cell and cell-free biosensors[J]. Analytical Chemistry, 2019, 91(23): 15284-15292.
31	MEYER A J, SEGALL-SHAPIRO T H, GLASSEY E, et al. Escherichia coli "Marionette" strains with 12 highly optimized small-molecule sensors[J]. Nature Chemical Biology, 2019, 15(2): 196-204.
32	GROSECLOSE T M, RONDON R E, HERDE Z D, et al. Engineered systems of inducible anti-repressors for the next generation of biological programming[J]. Nature Communications, 2020, 11(1): 4440.
33	HANKO E K R, PAIVA A C, JONCZYK M, et al. A genome-wide approach for identification and characterisation of metabolite-inducible systems[J]. Nature Communications, 2020, 11(1): 1213.
34	SCOTT S R, HASTY J. Quorum sensing communication modules for microbial consortia[J]. ACS Synthetic Biology, 2016, 5(9): 969-977.
35	GRANT P K, DALCHAU N, BROWN J R, et al. Orthogonal intercellular signaling for programmed spatial behavior[J]. Molecular Systems Biology, 2016, 12(1): 849.
36	KYLILIS N, TUZA Z A, STAN G B, et al. Tools for engineering coordinated system behaviour in synthetic microbial consortia[J]. Nature Communications, 2018, 9(1): 2677.
37	WU S B, QIAO J J, YANG A D, et al. Potential of orthogonal and cross-talk quorum sensing for dynamic regulation in cocultivation[J]. Chemical Engineering Journal, 2022, 445: 136720.
38	DU P, ZHAO H W, ZHANG H Q, et al. De novo design of an intercellular signaling toolbox for multi-channel cell-cell communication and biological computation[J]. Nature Communications, 2020, 11(1): 4226.
39	JONKERGOUW C, SAVOLA P, OSMEKHINA E, et al. Exploration of chemical diversity in intercellular quorum sensing signalling systems in prokaryotes[J]. Angewandte Chemie International Edition, 2024, 63(2): e202314469.
40	ESPAH BORUJENI A, MISHLER D M, WANG J Z, et al. Automated physics-based design of synthetic riboswitches from diverse RNA aptamers[J]. Nucleic Acids Research, 2016, 44(1): 1-13.
41	CHAPPELL J, WESTBROOK A, VEROSLOFF M, et al. Computational design of small transcription activating RNAs for versatile and dynamic gene regulation[J]. Nature Communications, 2017, 8(1): 1051.
42	DIDOVYK A, BOREK B, HASTY J, et al. Orthogonal modular gene repression in Escherichia coli using engineered CRISPR/Cas9[J]. ACS Synthetic Biology, 2016, 5(1): 81-88.
43	NIELSEN A A K, VOIGT C A. Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks[J]. Molecular Systems Biology, 2014, 10(11): 763.
44	REIS A C, HALPER S M, VEZEAU G E, et al. Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays[J]. Nature Biotechnology, 2019, 37(11): 1294-1301.
45	LIU Y, WAN X Y, WANG B J. Engineered CRISPRa enables programmable eukaryote-like gene activation in bacteria[J]. Nature Communications, 2019, 10(1): 3693.
46	GREEN A A, SILVER P A, COLLINS J J, et al. Toehold switches: de-novo-designed regulators of gene expression[J]. Cell, 2014, 159(4): 925-939.
47	KIM J M, ZHOU Y, CARLSON P D, et al. De novo-designed translation-repressing riboregulators for multi-input cellular logic[J]. Nature Chemical Biology, 2019, 15(12): 1173-1182.
48	PINTO F, THORNTON E L, WANG B J. An expanded library of orthogonal split inteins enables modular multi-peptide assemblies[J]. Nature Communications, 2020, 11(1): 1529.
49	CERVETTINI D, TANG S, FRIED S D, et al. Rapid discovery and evolution of orthogonal aminoacyl-tRNA synthetase-tRNA pairs[J]. Nature Biotechnology, 2020, 38(8): 989-999.
50	DUNKELMANN D L, WILLIS J C W, BEATTIE A T, et al. Engineered triply orthogonal pyrrolysyl-tRNA synthetase/tRNA pairs enable the genetic encoding of three distinct non-canonical amino acids[J]. Nature Chemistry, 2020, 12(6): 535-544.
51	MERRICK C A, ZHAO J, ROSSER S J. Serine integrases: advancing synthetic biology[J]. ACS Synthetic Biology, 2018, 7(2): 299-310.
52	PAGET M S. Bacterial sigma factors and anti-sigma factors: structure, function and distribution[J]. Biomolecules, 2015, 5(3): 1245-1265.
53	GUET C C, ELOWITZ M B, HSING W, et al. Combinatorial synthesis of genetic networks[J]. Science, 2002, 296(5572): 1466-1470.
54	HASTY J, DOLNIK M, ROTTSCHÄFER V, et al. Synthetic gene network for entraining and amplifying cellular oscillations[J]. Physical Review Letters, 2002, 88(14): 148101.
55	HOOSHANGI S, THIBERGE S, WEISS R. Ultrasensitivity and noise propagation in a synthetic transcriptional cascade[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(10): 3581-3586.
56	WANG B J, BARAHONA M, BUCK M. Amplification of small molecule-inducible gene expression via tuning of intracellular receptor densities[J]. Nucleic Acids Research, 2015, 43(3): 1955-1964.
57	李晓萌, 姜威, 梁泉峰, 等. 细菌群体感应系统在细胞间通讯中的应用及其合成生物学研究进展[J]. 合成生物学, 2020, 1(5): 540-555.
	LI X M, JIANG W, LIANG Q F, et al. Application of bacterial quorum sensing system in intercellular communication and its progress in synthetic biology[J]. Synthetic Biology Journal, 2020, 1(5): 540-555.
58	周爱林, 刘奕, 巴方, 等. 细菌群体感应元件构建和工程应用[J]. 合成生物学, 2021, 2(2): 234-246.
	ZHOU A L, LIU Y, BA F, et al. Construction and engineering application of bacterial quorum sensing elements[J]. Synthetic Biology Journal, 2021, 2(2): 234-246.
59	AMERUOSO A, GAMBILL L, LIU B Y, et al. Brave new 'RNA' world—advances in RNA tools and their application for understanding and engineering biological systems[J]. Current Opinion in Systems Biology, 2019, 14: 32-40.
60	ISAACS F J, DWYER D J, DING C M, et al. Engineered riboregulators enable post-transcriptional control of gene expression[J]. Nature Biotechnology, 2004, 22(7): 841-847.
61	付宪, 林涛, 张帆, 等. 基因密码子拓展技术的方法原理和前沿应用研究进展[J]. 合成生物学, 2020, 1(1): 103-119.
	FU X, LIN T, ZHANG F, et al. Progress in the study of genetic code expansion related methods, principles and applications[J]. Synthetic Biology Journal, 2020, 1(1): 103-119.
62	CANTON B, LABNO A, ENDY D. Refinement and standardization of synthetic biological parts and devices[J]. Nature Biotechnology, 2008, 26(7): 787-793.
63	CASINI A, STORCH M, BALDWIN G S, et al. Bricks and blueprints: methods and standards for DNA assembly[J]. Nature Reviews Molecular Cell Biology, 2015, 16(9): 568-576.
64	GUO Y K, DONG J K, ZHOU T, et al. YeastFab: the design and construction of standard biological parts for metabolic engineering in Saccharomyces cerevisiae [J]. Nucleic Acids Research, 2015, 43(13): e88.
65	RAJKUMAR A S, VARELA J A, JUERGENS H, et al. Biological parts for Kluyveromyces marxianus synthetic biology[J]. Frontiers in Bioengineering and Biotechnology, 2019, 7: 97.
66	WEBER E, ENGLER C, GRUETZNER R, et al. A modular cloning system for standardized assembly of multigene constructs[J]. PLoS One, 2011, 6(2): e16765.
67	LEE M E, DELOACHE W C, CERVANTES B, et al. A highly characterized yeast toolkit for modular, multipart assembly[J]. ACS Synthetic Biology, 2015, 4(9): 975-986.
68	WEI P L, FAN J, YU J W, et al. Quantitative characterization of filamentous fungal promoters on a single-cell resolution to discover cryptic natural products[J]. Science China Life Sciences, 2023, 66(4): 848-860.
69	REDDEN H, ALPER H S. The development and characterization of synthetic minimal yeast promoters[J]. Nature Communications, 2015, 6: 7810.
70	CURRAN K A, MORSE N J, MARKHAM K A, et al. Short synthetic terminators for improved heterologous gene expression in yeast[J]. ACS Synthetic Biology, 2015, 4(7): 824-832.
71	YIM S S, AN S J, KANG M, et al. Isolation of fully synthetic promoters for high-level gene expression in Corynebacterium glutamicum [J]. Biotechnology and Bioengineering, 2013, 110(11): 2959-2969.
72	MCLAUGHLIN J A, MYERS C J, ZUNDEL Z, et al. SynBioHub: a standards-enabled design repository for synthetic biology[J]. ACS Synthetic Biology, 2018, 7(2): 682-688.
73	BUSON F, GAO Y L, WANG B J. Genetic parts and enabling tools for biocircuit design[J]. ACS Synthetic Biology, 2024, 13(3): 697-713.
74	HILLSON N, CADDICK M, CAI Y Z, et al. Building a global alliance of biofoundries[J]. Nature Communications, 2019, 10(1): 2040.
75	GRUNBERG T W, DEL VECCHIO D. Modular analysis and design of biological circuits[J]. Current Opinion in Biotechnology, 2020, 63: 41-47.
76	WANG B J, BARAHONA M, BUCK M. Engineering modular and tunable genetic amplifiers for scaling transcriptional signals in cascaded gene networks[J]. Nucleic Acids Research, 2014, 42(14): 9484-9492.
77	LI Y Q, JIANG Y, CHEN H, et al. Modular construction of mammalian gene circuits using TALE transcriptional repressors[J]. Nature Chemical Biology, 2015, 11(3): 207-213.
78	LEVINE M, CATTOGLIO C, TJIAN R. Looping back to leap forward: transcription enters a new era[J]. Cell, 2014, 157(1): 13-25.
79	HOU J R, ZENG W Q, ZONG Y Q, et al. Engineering the ultrasensitive transcription factors by fusing a modular oligomerization domain[J]. ACS Synthetic Biology, 2018, 7(5): 1188-1194.
80	PURNICK P E M, WEISS R. The second wave of synthetic biology: from modules to systems[J]. Nature Reviews Molecular Cell Biology, 2009, 10(6): 410-422.
81	CARDINALE S, ARKIN A P. Contextualizing context for synthetic biology: identifying causes of failure of synthetic biological systems[J]. Biotechnology Journal, 2012, 7(7): 856-866.
82	DEL VECCHIO D, NINFA A J, SONTAG E D. Modular cell biology: retroactivity and insulation[J]. Molecular Systems Biology, 2008, 4: 161.
83	LIU Q J, SCHUMACHER J, WAN X Y, et al. Orthogonality and burdens of heterologous AND gate gene circuits in E.coli [J]. ACS Synthetic Biology, 2018, 7(2): 553-564.
84	CERONI F, ALGAR R, STAN G B, et al. Quantifying cellular capacity identifies gene expression designs with reduced burden[J]. Nature Methods, 2015, 12(5): 415-418.
85	SEGALL-SHAPIRO T H, SONTAG E D, VOIGT C A. Engineered promoters enable constant gene expression at any copy number in bacteria[J]. Nature Biotechnology, 2018, 36(4): 352-358.
86	WAN X Y, PINTO F, YU L Y, et al. Synthetic protein-binding DNA sponge as a tool to tune gene expression and mitigate protein toxicity[J]. Nature Communications, 2020, 11(1): 5961.
87	MISHRA D, RIVERA P M, LIN A, et al. A load driver device for engineering modularity in biological networks[J]. Nature Biotechnology, 2014, 32(12): 1268-1275.
88	WAN X Y, VOLPETTI F, PETROVA E, et al. Cascaded amplifying circuits enable ultrasensitive cellular sensors for toxic metals[J]. Nature Chemical Biology, 2019, 15(5): 540-548.
89	LIU Y, PINTO F, WAN X Y, et al. Reprogrammed tracrRNAs enable repurposing of RNAs as crRNAs and sequence-specific RNA biosensors[J]. Nature Communications, 2022, 13(1): 1937.
90	REGOT S, MACIA J, CONDE N, et al. Distributed biological computation with multicellular engineered networks[J]. Nature, 2011, 469(7329): 207-211.
91	AUSLÄNDER D, AUSLÄNDER S, PIERRAT X, et al. Programmable full-adder computations in communicating three-dimensional cell cultures[J]. Nature Methods, 2018, 15(1): 57-60.
92	DAVIS R M, MULLER R Y, HAYNES K A. Can the natural diversity of quorum-sensing advance synthetic biology?[J]. Frontiers in Bioengineering and Biotechnology, 2015, 3: 30.
93	CURATOLO A I, ZHOU N, ZHAO Y, et al. Cooperative pattern formation in multi-component bacterial systems through reciprocal motility regulation[J]. Nature Physics, 2020, 16: 1152-1157.
94	周楠, 夏婷颖, 黄建东. 合成生物学在探索生物图案形成基本原理中的应用与展望[J]. 合成生物学, 2020, 1(4): 470-480.
	ZHOU N, XIA T Y, HUANG J D. Applications and prospects of synthetic biology in exploring the basic principles of biological pattern formation[J]. Synthetic Biology Journal, 2020, 1(4): 470-480.
95	KHAN M S, KIM E, MCPHERSON A, et al. Adenovirus-vectored SARS-CoV-2 vaccine expressing S1-N fusion protein[J]. Antibody Therapeutics, 2022, 5(3): 177-191.
96	LI H S, ISRANI D V, GAGNON K A, et al. Multidimensional control of therapeutic human cell function with synthetic gene circuits[J]. Science, 2022, 378(6625): 1227-1234.
97	唐婷, 付立豪, 郭二鹏, 等. 自动化合成生物技术与工程化设施平台[J]. 科学通报, 2021, 66(3): 300-309.
	TANG T, FU L H, GUO E P, et al. Automation in synthetic biology using biological foundries[J]. Chinese Science Bulletin, 2021, 66(3): 300-309.
98	CAMACHO D M, COLLINS K M, POWERS R K, et al. Next-generation machine learning for biological networks[J]. Cell, 2018, 173(7): 1581-1592.
99	NIELSEN A A K, DER B S, SHIN J, et al. Genetic circuit design automation[J]. Science, 2016, 352(6281): aac7341.
100	TAKETANI M, ZHANG J B, ZHANG S Y, et al. Genetic circuit design automation for the gut resident species Bacteroides thetaiotaomicron [J]. Nature Biotechnology, 2020, 38(8): 962-969.
101	CHEN Y, ZHANG S Y, YOUNG E M, et al. Genetic circuit design automation for yeast[J]. Nature Microbiology, 2020, 5(11): 1349-1360.
102	JONES T S, OLIVEIRA S M D, MYERS C J, et al. Genetic circuit design automation with Cello 2.0[J]. Nature Protocols, 2022, 17(4): 1097-1113.
103	MYERS C J, BARKER N, JONES K, et al. iBioSim: a tool for the analysis and design of genetic circuits[J]. Bioinformatics, 2009, 25(21): 2848-2849.
104	WATANABE L, NGUYEN T, ZHANG M, et al. iBioSim 3: a tool for model-based genetic circuit design[J]. ACS Synthetic Biology, 2019, 8(7): 1560-1563.
105	HILLSON N J, ROSENGARTEN R D, KEASLING J D. j5 DNA assembly design automation software[J]. ACS Synthetic Biology, 2012, 1(1): 14-21.
106	WILSON M L, HERTZBERG R, ADAM L, et al. A step-by-step introduction to rule-based design of synthetic genetic constructs using GenoCAD[J]. Methods in Enzymology, 2011, 498: 173-188.
107	HOLOWKO M B, FROW E K, REID J C, et al. Building a biofoundry[J]. Synthetic Biology, 2021, 6(1): ysaa026.
108	CHAO R, LIANG J, TASAN I, et al. Fully automated one-step synthesis of single-transcript TALEN pairs using a biological foundry[J]. ACS Synthetic Biology, 2017, 6(4): 678-685.
109	SI T, CHAO R, MIN Y H, et al. Automated multiplex genome-scale engineering in yeast[J]. Nature Communications, 2017, 8: 15187.
110	崔金明, 张炳照, 马迎飞, 等. 合成生物学研究的工程化平台[J]. 中国科学院院刊, 2018, 33(11): 1249-1257.
	CUI J M, ZHANG B Z, MA Y F, et al. Engineering platforms for synthetic biology research[J]. Bulletin of Chinese Academy of Sciences, 2018, 33(11): 1249-1257.
111	ZHANG S Y, ZHU J, FAN S, et al. Directed evolution of a cyclodipeptide synthase with new activities via label-free mass spectrometric screening[J]. Chemical Science, 2022, 13(25): 7581-7586.
112	GUO E P, FU L H, FANG X T, et al. Robotic construction and screening of lanthipeptide variant libraries in Escherichia coli [J]. ACS Synthetic Biology, 2022, 11(12): 3900-3911.
113	ZHANG J Z, LI T, HONG Z L, et al. Biosynthesis of hybrid neutral lipids with archaeal and eukaryotic characteristics in engineered Saccharomyces cerevisiae [J]. Angewandte Chemie International Edition, 2023, 62(4): e202214344.
114	DENG H X, YU H, DENG Y W, et al. Pathway evolution through a bottlenecking-debottlenecking strategy and machine learning-aided flux balancing[J]. Advanced Science, 2024: e2306935.
115	卢挥, 张芳丽, 黄磊. 合成生物学自动化装置iBioFoundry的构建与应用[J]. 合成生物学, 2023, 4(5): 877-891.
	LU H, ZHANG F L, HUANG L. Establishment of iBioFoundry for synthetic biology applications[J]. Synthetic Biology Journal, 2023, 4(5): 877-891.
116	HU R Y, FU L H, CHEN Y C, et al. Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments[J]. Briefings in Bioinformatics, 2023, 24(1): bbac570.
117	WANG Y, WANG H C, WEI L, et al. Synthetic promoter design in Escherichia coli based on a deep generative network[J]. Nucleic Acids Research, 2020, 48(12): 6403-6412.
118	CONSTANT D A, GUTIERREZ J M, SASTRY A V, et al. Deep learning-based codon optimization with large-scale synonymous variant datasets enables generalized tunable protein expression[EB/OL]. bioRxiv, 2023, 2023.02.11.528149. (2023-02-12)[2023-12-01]. .
119	ALIPANAHI B, DELONG A, WEIRAUCH M T, et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning[J]. Nature Biotechnology, 2015, 33(8): 831-838.
120	WANG M, TAI C, E W N, et al. DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants[J]. Nucleic Acids Research, 2018, 46(11): e69.
121	VALERI J A, COLLINS K M, RAMESH P, et al. Sequence-to-function deep learning frameworks for engineered riboregulators[J]. Nature Communications, 2020, 11(1): 5058.
122	SEAK L C U, LO O L I, SUEN W, et al. Next-generation biocomputing: mimicking artificial neural network with genetic circuits[EB/OL]. bioRxiv, 2021, 2021.03.12.435120. (2021-03-12)[2023-12-01]. .

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