Progress and prospective of engineering microbial cell factories： from random mutagenesis to customized design in genome scale

doi:10.12211/2096-8208.2020-050

Abstract

Abstract:

As the core of green bio-manufacturing and bioeconomy, microbial cell factories (MCFs) are widely used to produce a variety of chemicals, foods, medicines and fuels. In the early years, isolation and random mutagenesis of natural microbes were time-consuming but widely used for developing well-performed MCFs. With the development of molecular biology and genetic engineering, the advancements in understanding of microbial systems prompted the establishment of metabolic engineering. Nowadays, different MCF-construction strategies in terms of protein, pathway and genome-wide engineering have been well developed based on rational or semi-rational metabolic engineering strategies. However, due to limited biological knowledge, these strategies mainly rely on the iterative cycle of ‘Design-Build-Test-Learning (DBTL)’, usually taking 50—300 person-years and hundreds of millions of dollars to develop a MCF that can meet industrial demands. Combining high-throughput genome editing and phenotype screening and selection technologies, the genome-wide customized engineering allows one to obtain large-scale genotype-phenotype association (GPA) data sets quickly. Based on these results, data science technologies can be further applied to mine a large number of unknown genes or loci associated with the specific phenotypes. This strategy has a wider search scope for genotype (genome-wide) and does not rely on existing biological knowledge (data-driven), thus making it possible to explore phenotypes that could not be achieved by the above mentioned rational/semi-rational strategies and develop MCFs with superior performance more efficiently. This paper reviews general strategies and application cases for the design and construction of MCFs. We will firstly summarize the overview of random mutagenesis strategies, and the history and latest progress in metabolic engineering for the construction of MCFs. Then we discuss the potential of the newly emerging MCF-design and construction paradigm, and the customized design strategies at the whole genome scales. Finally, we conclude with our perspectives on the development of novel strategies for the engineering of MCFs.

Key words: Bio-manufacturing, microbial cell factories, metabolic engineering, genotype-phenotype associations, data-driven

摘要：

微生物细胞工厂（microbial cell factories，MCFs）被广泛用于生产丰富多样的化学品、食品、药品和能源，是绿色生物制造的核心环节。早期主要通过天然微生物的筛选和诱变育种的方式获得高产菌种，然而作为一种“以时间（人力）换水平”的非理性策略，其创制效率极低。随着分子生物学和基因工程研究方法的不断发展，对微生物系统认知和改造能力的进步促使代谢工程学科诞生。基于生物学知识的理性/半理性代谢工程设计和构建策略，目前已发展了从分子、途径到基因组层次不同的MCFs设计和工程化构建策略。本文结合实际案例对MCFs的设计及构建策略进行综述，首先回顾传统诱变育种和代谢工程指导的理性/半理性设计策略，探讨如何突破代谢工程经典框架的限制，实现全基因组水平定制化MCFs的快速构建，最后对这一新的构建范式的未来进行展望。

关键词: 生物制造, 微生物细胞工厂, 代谢工程, 基因型-表型关联, 数据驱动

CLC Number:

YUAN Yaomeng, XING Xinhui, ZHANG Chong. Progress and prospective of engineering microbial cell factories： from random mutagenesis to customized design in genome scale[J]. Synthetic Biology Journal, 2020, 1(6): 656-673.

袁姚梦, 邢新会, 张翀. 微生物细胞工厂的设计构建：从诱变育种到全基因组定制化创制[J]. 合成生物学, 2020, 1(6): 656-673.

Figures/Tables 9

References 143

1	NIELSEN J. Cell factory engineering for improved production of natural products[J]. Natural Product Reports, 2019, 36(9): 1233-1236.
2	SANO C. History of glutamate production[J]. The American Journal of Clinical Nutrition, 2009, 90(3), 728S-732S.
3	OTERO J M, NIELSEN J. Industrial systems biology[J]. Biotechnology and Bioengineering, 2010, 105(3), 439-460.
4	Presidential green chemistry challenge awards[EB/OL]. .
5	ATSUMI S, HIGASHIDE W, LIAO J C. Direct photosynthetic recycling of carbon dioxide to isobutyraldehyde[J]. Nature Biotechnology, 2009, 27(12), 1177-1180.
6	GALANIE S, THODEY K, TRENCHARD I J, et al. Complete biosynthesis of opioids in yeast[J]. Science, 2015, 349(6252): 1095-1100.
7	PARK J H, LEE K H, KIM T Y, et al. Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(19): 7797-7802.
8	XIA X X, QIAN Z G, KI C S, et al. Native-sized recombinant spider silk protein produced in metabolically engineered Escherichia coli results in a strong fiber[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(32):14059-14063.
9	STEEN E J, KANG Y, BOKINSKY G, et al. Microbial production of fatty-acid-derived fuels and chemicals from plant biomass[J]. Nature, 2010, 463(7280): 559-562.
10	LEE G, NA J. Future of microbial polyesters[J]. Microbial Cell Factories, 2013, 12(1): 54.
11	BROWN S W, OLIVER S G. Isolation of ethanol-tolerant mutants of yeast by continuous selection[J]. European Journal of Applied Microbiology and Biotechnology, 1982, 16(2/3): 119-122.
12	MEADOWS L, HAWKINS K M, TSEGAYE Y, et al. Rewriting yeast central carbon metabolism for industrial isoprenoid production[J]. Nature, 2016, 537(7622): 694-697.
13	WEIKERT C, SAUER U, BAILEY J. Use of a glycerol-limited, long-term chemostat for isolation of Escherichia coli mutants with improved physiological properties[J]. Microbiology, 1997, 143(5): 1567-1574.
14	SILMAN N J, CARVER M A, JONES C W. Physiology of amidase production by methylophilus methylotrophus: isolation of hyperactive strains using continuous culture[J]. Microbiology, 1989, 135(11): 3153-3164.
15	CORNISH A, GREENWOOD J A, JONES C W. Binding-protein-dependent sugar transport by Agrobacterium radiobacter and A. tumefaciens grown in continuous culture[J]. Microbiology, 1989, 135(11): 3001-3013.
16	BAILEY J. Toward a science of metabolic engineering[J]. Science, 1991, 252(5013): 1668-1675.
17	STEPHANOPOULOS G, VALLINO J. Network rigidity and metabolic engineering in metabolite overproduction[J]. Science, 1991, 252(5013): 1675-1681.
18	STEPHANOPOULOS G, SINSKEY A J. Metabolic engineering-methodologies and future prospects[J]. Trends in Biotechnology, 1993, 11(9): 392-396.
19	FURUSAWA C, HORINOUCHI T, HIRSAWA T, et al. Systems metabolic engineering: the creation of microbial cell factories by rational metabolic design and evolution[J]. Advances in Biochemical Engineering/Biotechnology, 2012, 131(3): 1-23.
20	NIELSEN J. Production of bio-pharmaceutical proteins by yeast: advances through metabolic engineering[J]. Bioengineered, 2012, 4(4): 207-211.
21	HONG K K, NIELSEN J. Metabolic engineering of Saccharomyces cerevisiae: a key cell factory platform for future biorefineries[J]. Cellular and Molecular Life Sciences, 2012, 69(16),2671-2690.
22	CHEN Z, RAPPERT S, SUN J, et al. Integrating molecular dynamics and co-evolutionary analysis for reliable target prediction and deregulation of the allosteric inhibition of aspartokinase for amino acid production[J]. Journal of Biotechnology, 2011, 154(4): 248-254.
23	CHO B K, FEDEROWICZ S, PARK Y S, et al. Deciphering the transcriptional regulatory logic of amino acid metabolism[J]. Nature Chemical Biology, 2011, 8(1): 65-71.
24	JIN Y S, STEPHANOPOULOS G. Multi-dimensional gene target search for improving lycopene biosynthesis in Escherichia coli [J]. Metabolic Engineering, 2007, 9(4): 337-347.
25	ÖZAYDIN B, BURD H, LEE T S, et al. Carotenoid-based phenotypic screen of the yeast deletion collection reveals new genes with roles in isoprenoid production[J]. Metabolic Engineering, 2013, 15: 174-183.
26	ZHANG Y X, PERRY K, VINCI V A, et al. Genome shuffling leads to rapid phenotypic improvement in bacteria[J]. Nature, 2002, 415(6872), 644-646.
27	ZHANG X, ZHANG X F, LI H P, et al. Atmospheric and room temperature plasma (ARTP) as a new powerful mutagenesis tool[J]. Applied Microbiology and Biotechnology, 2014, 98(12): 5387-5396.
28	NICOLAOU S A, GAIDA S M, PAPOUTSAKIS E T. Coexisting/Coexpressing genomic libraries (CoGeL) identify interactions among distantly located genetic loci for developing complex microbial phenotypes[J]. Nucleic Acids Research, 2011, 39(22): e152.
29	WANG T, CHANG G G, JIA H G, et al. Pooled CRISPR interference screening enables genome-scale functional genomics study in bacteria with superior performance[J]. Nature Communications, 2018, 9(1):2475.
30	李翔. 微生物诱变育种技术[J]. 现代商贸工业, 2017(34): 185-187.
	LI X. Microbial mutation breeding technology[J]. Modern Business Trade Industry, 2017(34): 185-187.
31	DEMAIN A L. From natural products discovery to commercialization: a success story[J]. Journal of Industrial Microbiology & Biotechnology, 2006, 33(7), 486-495.
32	季宇彬, 朱兴杰, 徐昶儒, 等. 微生物诱变育种方法的研究进展[C]// 中国毒理学会环境与生态毒理学专业委员会成立大会. 2008.
	JI Y B, ZHU X J, XU C R, et al. Research progress of microbial mutation breeding methods [C] // The Founding Conference of the Professional Committee of Environmental and Ecotoxicology of the Chinese Society of Toxicology. 2008.
33	微生物诱变育种编写组.微生物诱变育种[M].北京:科学出版社, 1973: 25-32.
	Microbiology Mutation Breeding Compilation Group. Microbial mutation breeding [M]. Beijing: Science Press, 1973: 25-32.
34	NESS J E, WELCH M, GIVER L, et al. DNA shuffling of subgenomic sequences of subtilisin[J]. Nature Biotechnology, 1999, 17(9): 893-896.
35	施巧琴, 吴松钢. 工业微生物育种[M]. 福州: 福建科学技术出版社, 1991: 54-55.
	SHI Q Q, WU S G. Breeding of industrial microbes[M]. Fuzhou: Fujian Science and Technology Publishing House, 1991: 54-55.
36	CVIJOVIC M, BORDEL S, NIELSEN J. Mathematical models of cell factories: moving towards the core of industrial biotechnology[J]. Microbial Biotechnology, 2010, 4(5): 572-584.
37	FELL D A. Metabolic control analysis: a survey of its theoretical and experimental development[J]. Biochemical Journal, 1992, 286(2): 313-330.
38	WIECHERT W. ¹³C metabolic flux analysis[J]. Metabolic Engineering, 2001, 3(3): 195-206.
39	STEPHANOPOULOS G. Metabolic fluxes and metabolic engineering[J]. Metabolic Engineering, 1999, 1(1): 1-11.
40	VARMA A, PALSSON B O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110[J]. Applied and Environmental Microbiology, 1994, 60 (10): 3724-3731.
41	PFEIFFER T, SANCHEZ-VALDENEBRO I, NUNO J, et al. METATOOL: for studying metabolic networks[J]. Bioinformatics, 1999, 15(3): 251-257.
42	TRINH C T, WLASCHIN A, SRIENC F. Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism[J]. Applied Microbiology and Biotechnology, 2009, 81(5): 813-826.
43	ZAMBONI N, KÜMMEL A, HEINEMANN M. anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data[J]. BMC Bioinformatics, 2008, 9(1): 199.
44	HENRY C S, BROADBELT L J, HATZIMANIKATIS V. Thermodynamics-based metabolic flux analysis[J]. Biophysical Journal, 2007, 92(5), 1792-1805.
45	BECKER S A, FEIST A M, MO M L, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox[J]. Nature Protocols, 2007, 2(3): 727-738.
46	SEGRE D, VITKUP D, CHURCH G M. Analysis of optimality in natural and perturbed metabolic networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(23): 15112-15117.
47	SHLOMI T, BERKMAN O, RUPPIN E. Regulatory on/off minimization of metabolic flux changes after genetic perturbations[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(21): 7695-7700.
48	BURGARD A P, PHARKYA P, MARANAS C D. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization[J]. Biotechnology and Bioengineering, 2003, 84(6): 647-657.
49	HATZIMANIKATIS V, LI C IONITA J A, et al. Exploring the diversity of complex metabolic networks[J]. Bioinformatics, 2004, 21(8): 1603-1609.
50	EDWARDS J S, PALSSON B O. The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities[J]. Proceedings of the National Academy of Sciences of the United States of America, 2000, 97(10): 5528-5533.
51	COVERT M W, SCHILLING C H, FAMILI I, et al. Metabolic modeling of microbial strains in silico[J]. Trends in Biochemical Sciences, 2001, 26(3): 179-186.
52	ÖSTERLUND T, NOOKAEW I, NIELSEN J. Fifteen years of large scale metabolic modeling of yeast: developments and impacts[J]. Biotechnology Advances, 2012, 30(5): 979-988.
53	FÖRSTER J, FAMILI I, FU P, et al. Genome-Scale Reconstruction of the Saccharomyces cerevisiae metabolic network[J]. Genome Research, 2003, 13(2): 244-253.
54	VARMA A, BOESCH B W, PALSSON B O. Biochemical production capabilities of Escherichia coli [J]. Biotechnology and Bioengineering, 1993, 42(1): 59-73.
55	VALLINO J J, STEPHANOPOULOS G. Carbon flux distributions at the glucose 6-phosphate branch point in Corynebacterium glutamicum during lysine overproduction[J]. Biotechnology Progress, 1994, 10(3): 327-334.
56	GULIK W M VAN, HEIJNEN J J. A metabolic network stoichiometry analysis of microbial growth and product formation[J]. Biotechnology and Bioengineering, 1995, 48(6): 681-698.
57	VANROLLEGHEM P A, DE JONG-GUBBELS P, GULIK W M VAN, et al. Validation of a metabolic network for Saccharomyces cerevisiae using mixed substrate studies[J]. Biotechnology Progress, 1996, 12(4): 434-448.
58	NISSEN T L, SCHULZE U, NIELSEN J, et al. Flux distributions in anaerobic, glucose-limited continuous cultures of Saccharomyces cerevisiae [J]. Microbiology, 1997, 143(1): 203-218.
59	DUARTE N C. Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model[J]. Genome Research, 2004, 14(7): 1298-1309.
60	DOBSON P D, SMALLBONE K, JAMESON D, et al. Further developments towards a genome-scale metabolic model of yeast[J]. BMC Systems Biology, 2010, 4(1): 145.
61	ORTH J D, CONRAD T M, NA J, et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011[J]. Molecular Systems Biology, 2011, 7(1): 535.
62	KJELDSEN K R, NIELSEN J. In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network[J]. Biotechnology and Bioengineering, 2009, 102(2): 583-597.
63	OLIVEIRA A, NIELSEN J, FORSTER J. Modeling Lactococcus lactis using a genome-scale flux model[J]. BMC Microbiology, 2005, 5(1): 39.
64	PARK S, SCHILLING C, PALSSON B O : Compositions and methods for modeling Bacillus subtilis metabolism: US 20030224363[P]. 2003-12-04.
65	LERMAN J A, HYDUKE D R, LATIF H, et al. In silico method for modelling metabolism and gene product expression at genome scale[J]. Nature Communications, 2012, 3(1): 929.
66	LIU J K, O'BRIEN E J, LERMAN J A, et al. Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale[J]. BMC Systems Biology, 2014, 8:110.
67	ZHANG Y, THIELE I, WEEKES D, et al. Three-dimensional structural view of the central metabolic network of Thermotoga maritima [J]. Science, 2009, 325(5947): 1544-1549.
68	CHANG R L, ANDREWS K, KIM D, et al. Structural systems biology evaluation of metabolic thermotolerance in Escherichia coli [J]. Science, 2013, 340(6137): 1220-1223.
69	CHANG R L, XIE L, BOURNE P E, et al. Antibacterial mechanisms identified through structural systems pharmacology[J]. BMC Systems Biology, 2013, 7(1): 102.
70	CHANDRASEKARAN S, PRICE N D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis [J]. Proceedings of the National Academy Sciences of the United States of America, 2010, 107(41): 17845-17850.
71	HERRGARD M J. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae [J]. Genome Research, 2006, 16(5): 627-635.
72	KARR J R, SANGHVI J C, MACKLIN D N, et al. A whole-cell computational model predicts phenotype from genotype[J]. Cell, 2012, 150(2): 389-401.
73	BECKER J, ZELDER O, HäFNER STEFAN, et al. From zero to hero-design-based systems metabolic engineering of Corynebacterium glutamicum for L-lysine production[J]. Metabolic Engineering, 2011, 13(2): 159-168.
74	CHOI K R, JANG W D, YANG D, et al. Systems metabolic engineering strategies: integrating systems and synthetic biology with metabolic engineering[J]. Trends in Biotechnology, 2019, 37(8): 817-837.
75	JANSET M L, GULIK W M VAN. Towards large scale fermentative production of succinic acid[J]. Current Opinion in Biotechnology, 2014,30: 190-197.
76	BURK M J. Sustainable production of industrial chemicals from sugars[J]. International Sugar Journal, 2010, 112(1333): 30-35.
77	DATTA J, KASPRZYK P, BŁAŻEK K, et al. Synthesis, structure and properties of poly(ester-urethane)s obtained using bio-based and petrochemical 1,3-propanediol and 1,4-butanediol[J]. Journal of Thermal Analysis and Calorimetry, 2017, 130: 261-276.
78	NAKAMURA C E, WHITE G M. Metabolic engineering for the microbial production of 1,3-propanediol[J]. Current Opinion in Biotechnology, 2003, 14(5): 454-459.
79	Yield 10 bioscience agricultural company-Corporate Overview[OL]. .
80	BECKER J, LANGE A, FABARIUS J. Top value platform chemicals: bio-based production of organic acids[J]. Current Opinion in Biotechnology, 2015, 36: 168-175.
81	GUSTAVSSON M, LEE S Y. Prospects of microbial cell factories developed through systems metabolic engineering[J]. Microbial Biotechnology, 2016, 9(5): 610-617.
82	BUIJS N A, SIEWERS V, NIELSEN J. Advanced biofuel production by the yeast Saccharomyces cerevisiae [J]. Current Opinion in Chemical Biology, 2013, 17(3): 480-488.
83	GEORGE K W, ALONSO-GUTIERREZ J, KEASLING J D, et al. Isoprenoid drugs, biofuels, and chemicals-artemisinin, farnesene, and beyond[J]. Advances in Biochemical Engineering/Biotechnology, 2015, 355-389.
84	PADDON C J, KEASLING J D.Semi-synthetic artemisinin: a model for the use of synthetic biology inpharmaceutical development[J]. Nature Reviews Microbiology, 2014, 12(5): 355-367.
85	PADDON C J, WESTFALL P J, PITERA D J, et al. High level semi-synthetic production of the potent antimalarial artemisinin[J]. Nature, 2013, 496(7446): 528-532.
86	FREED E F, WINKLER J D, WEISS S J, et al. Genome-wide tuning of protein expression levels to rapidly engineer microbial traits[J]. ACS Synthetic Biology, 2015, 4(11): 1244-1253.
87	BABA T, ARA T, HASEGAWA M, et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection[J]. Molecular Systems Biology, 2006. DOI: 10.1038/msb4100050 .
88	KOO B M, KRITIKOS G, FARELLI J D, et al. Construction and analysis of two genome-scale deletion libraries for Bacillus subtilis [J]. Cell systems, 2017, 4(3): 291-305.
89	WANG H H, ISAACS F J, CARR P A, et al. Programming cells by multiplex genome engineering and accelerated evolution[J]. Nature, 2009, 460(7257): 894-898.
90	WARNER J R, REEDER P J, KARIMPOUR-FARD A, et al. Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides[J]. Nature Biotechnology, 2010, 28(8): 856-862.
91	DOUDNA J A, CHARPENTIER E. The new frontier of genome engineering with CRISPR-Cas9[J]. Science, 2014, 346(6213): 1077.
92	QI L S, LARSON M H, GILBERT L A, et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression[J]. Cell, 2013, 152(5): 1173-1183.
93	DONG C, FONTANA J, PATEL A, et al. Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria[J]. Nature Communications, 2018, 9(1): 2489.
94	MANS R, ROSSUM H M VAN, WIJSMAN M, et al. CRISPR/Cas9: a molecular Swiss army knife for simultaneous introduction of multiple genetic modifications in Saccharomyces cerevisiae [J]. FEMS Yeast Research, 2015,15(2). DOI: 1093/femsyr/fov004 .
95	HALPERIN S O, TOU C J, WONG E B, et al. CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window[J]. Nature, 2018, 560(7717): 248-252.
96	JIANG W, BIKARD D, COX D, et al. RNA-guided editing of bacterial genomes using CRISPR-Cas systems[J]. Nature Biotechnology, 2013, 31: 233-239.
97	OH J H, van PIJKEREN J P. CRISPR-Cas9-assisted recombineering in Lactobacillus reuteri [J]. Nucleic Acids Research 2014, 42: e131.
98	CHOUDHARY E, THAKUR P, PAREEK M, et al. Gene silencing by CRISPR interference in mycobacteria[J]. Nature Communications, 2015, 6(1): 6267.
99	YAO L, CENGIC I, ANFELT J, et al. Multiple gene repression in cyanobacteria using CRISPRi[J]. ACS Synthetic Biology 2016, 5: 207-212.
100	XU T, LI Y, SHI Z, et al. Efficient genome editing in Clostridium cellulolyticum via CRISPR-Cas9 nickase.[J]. Applied and Environmental Microbiology, 2015, 81: 4423-4431.
101	PETERS J M, COLAVIN A, SHI H, et al. A comprehensive, CRISPR-based functional analysis of essential genes in bacteria[J]. Cell, 2016, 165: 1493-1506.
102	JIANG Y, QIAN F, YANG J, et al. CRISPR-Cpf1 assisted genome editing of Corynebacterium glutamicum [J]. Nature Communications, 2017, 8(1): 15179.
103	NAYAK D D, METCALF W W. Cas9-mediated genome editing in the methanogenic archaeon Methanosarcina acetivorans [J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114: 2976-2981.
104	王天民.基于 CRISPR 干扰技术的微生物高通量功能基因组学方法[D]. 北京: 清华大学, 2018.
	WANG T M. CRISPR interference based high-throughput functional genomics platform method in bacteria[D]. Beijing: Tsinghua University, 2018.
105	DICARLO J E, CONLEY A J, PENTTILA M, et al. Yeast oligo-mediated genome engineering (YOGE)[J]. ACS Synthetic Biology, 2013, 2(12): 741-749.
106	GARST A D, BASSALO M C, PINES G,et al. Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering[J]. Nature Biotechnology, 2016, 35(1): 48-55.
107	ANZALONE A V, RANDOLPH P B, DAVIS J R, et al. Search-and-replace genome editing without double-strand breaks or donor DNA[J]. Nature, 2019, 576(7785): 149-157.
108	NISHIDA K, ARAZOE T, YACHIE N, et al. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems[J]. Science, 2016, 353(6305): aaf8729.
109	MA Y, ZHANG J, YIN W, et al. Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells[J]. Nature Methods, 2016, 13(12): 1029-1035.
110	HESS G T, FRESARD L, HAN K, et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells[J]. Nature Methods, 2016, 13(12): 1036-1042.
111	LI Q, SEO J H, STRANGER B, et al. Integrative eQTL-Based analyses reveal the biology of breast cancer risk loci[J]. Cell, 2013, 152(3): 633-641.
112	WU J, ZHOU P, ZHANG X, et al. Efficient de novo synthesis of resveratrol by metabolically engineered Escherichia coli [J]. Journal of Industrial Microbiology & Biotechnology, 2017, 44(7): 1083-1095.
113	GILBERT L A, LARSON M H, MORSUT L, et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes[J]. Cell, 2013, 154(2): 442-451.
114	HALPERIN S O, TOU C J, WANG E B, et al. CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window[J]. Nature, 2018, 560: 248-252.
115	WONG B G, MANCUSO C P, KIRIAKOV S, et al. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER[J]. Nature Biotechnology, 2018, 36(7): 614-623.
116	JIAN X, GUO X, WANG J, et al. Microbial microdroplet culture system (MMC): an integrated platform for automated, high-throughput microbial cultivation and adaptive evolution[J]. Biotechnology and Bioengineering, 2019. DOI: 10.1101/2019.12. 19.883561 .
117	MICHENER J K, THODEY K, LIANG J C, et al. Applications of genetically-encoded biosensors for the construction and control of biosynthetic pathways[J]. Metabolic Engineering, 2012, 14(3): 212-222.
118	LIU D, EVANS T, ZHANG F Z, et al. Applications and advances of metabolite biosensors for metabolic engineering[J]. Metabolic Engineering, 2015, 31: 15-22.
119	FANG M, WANG T, ZHANG C, et al. Intermediate-sensor assisted push-pull strategy and its application in heterologous deoxyviolacein production in Escherichia coli [J]. Metabolic Engineering, 2016, 33: 41-51.
120	LIANG W, CUI L, CUI J, et al. Biosensor-assisted transcriptional regulator engineering for Methylobacterium extorquens AM1 to improve mevalonate synthesis by increasing the acetyl-CoA supply[J]. Metabolic Engineering, 2017, 39: 159-168.
121	BINDER S, SCHENDZIELORZ G, STABLER N, et al. A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level[J]. Genome Biology, 2012, 13: R40.
122	BENEYTON T, THOMAS S, GRIFFITHS A D, et al. Droplet-based microfluidic high-throughput screening of heterologous enzymes secreted by the yeast Yarrowia lipolytica [J]. Microbial Cell Factories, 2017, 16(1): 18.
123	SIEDLER S, KHATRI N K, ZSOHAR A, et al. Development of a bacterial biosensor for rapid screening of yeast p-coumaric acid production[J]. ACS Synthetic Biology, 2017, 6(10): 1860-1869.
124	BARET J C, MILLER O J, TALY V, et al. Fluorescence-activated droplet sorting (FADS): efficient microfluidic cell sorting based on enzymatic activity[J]. Lab on a Chip, 2009, 9(13): 1850.
125	WAGNER J M, LIU L, YUAN S et. al. A comparative analysis of single cell and droplet-based FACS for improving production phenotypes : riboflavin overproduction in Yarrowia lipolytica [J]. Metabolic Engineering, 2018, 47: 346-356.
126	MA C, TAN Z L, LIN Y, et al. Gel microdroplet-based high-throughput screening for directed evolution of xylanase-producing Pichia pastoris [J]. Journal of Bioscience and Bioengineering, 2019, 128(6): 662-668.
127	WANG X, REN L, SU Y, et al. Raman-activated droplet sorting (RADS) for label-free high-throughput screening of microalgal single-cells[J]. Analytical Chemistry, 2017, 89(22): 12569-12577.
128	DIXIT A, PARNAS O, LI B, et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens[J]. Cell, 2016, 167: 1853-1866.
129	TROPINI C, EARLE K A, HUANG K C, et al. The gut microbiome: connecting spatial organization to function[J]. Cell Host Microbe, 2017, 21: 433-442.
130	SHI H, COLAVIN A, LEE T K, et al. Strain library imaging protocol for high-throughput, automated single-cell microscopy of large bacterial collections arrayed on multiwell plates[J]. Nature Protocols Erecipes for Researchers, 2017, 12: 429-438.
131	LAWSON M J, CAMSUND D, LARSSON J, et al. In situ genotyping of a pooled strain library after characterizing complex phenotypes[J]. Molecular Systems Biology, 2017, 13: 947.
132	ZONG Y, ZHANG H M, LYU C, et al. Insulated transcriptional elements enable precise design of genetic circuits[J]. Nature Communications, 2017, 8: 52.
133	BASSALO M C, GARST A D, CHOUDHURY A, et al. Deep scanning lysine metabolism in Escherichia coli [J]. Molecular Systems Biology, 2018, 14(11): e8371.
134	LIANG L, LIU R, GARST A D, et al. CRISPR EnAbled trackable genome engineering for isopropanol production in Escherichia coli [J]. Metabolic Engineering, 2017, 41: 1-10.
135	LIU R, LIANG L, GARST A D, et al. Directed combinatorial mutagenesis of Escherichia coli for complex phenotype engineering[J]. Metabolic Engineering, 2018, 47: 10-20.
136	LIANG L, LIU R, FOSTER K E, et al. Genome engineering of E. coli for improved styrene production[J]. Metabolic Engineering, 2020, 57: 74-84.
137	ANGERMUELLER C, PÄRNAMAA T, PARTS L, et al. Deep learning for computational biology[J]. Molecular Systems Biology, 2016, 12(7): 878.
138	COSTELLO Z, MARTIN H G. A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data[J]. NPJ Systems Biology and Applications, 2018, 4(1): 1-14.
139	CUPERUS J T, GROVES B, KUCHINA A, et al. Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences[J]. Genome Research, 2017, 27(12): 2015-2024.
140	ZHOU Y, LI G, DONG J, et al. MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae [J]. Metabolic Engineering, 2018, 47: 294-302.
141	DENBY C M, LI R A, VU V T, et al. Industrial brewing yeast engineered for the production of primary flavor determinants in hopped beer[J]. Nature Communications, 2018, 9(1): 965.
142	ZHANG J, PSTERSEN S D, RADIVOJEVIC T, et al. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism[J]. Nature Communications, 2020, 11(1): 4880.
143	GUO J, WANG T, GUAN C, et al. Improved sgRNA design in bacteria via genome-wide activity profiling[J]. Nucleic Acids Research, 2018, 46(14): 7052-7069.

分类	诱变技术/诱变剂	描述	参考文献
物理诱变	电离辐射（X射线、γ射线等）	引起DNA双链或单链断裂，实现DNA的删除或结构改变	[30]
	非电离辐射（紫外线）	使嘧啶形成二聚体，实现GC的删除、移码突变以及GC→AT的转换	[32]
化学诱变	烷化剂（烷基磺酸盐、芥子气等）	使DNA碱基发生烷化，导致DNA复制时发生配对错误	[32]
	碱基类似物（嘧啶类似物、嘌呤类似物）	与DNA碱基结构类似，在DNA复制时掺入并引发配对错误	[30]
	移码诱变剂（原黄素、吖啶橙等）	与DNA结合导致碱基增添或缺失	[30]
	脱氨剂（亚硝酸）	引起A、C、G碱基的脱氨，实现GC与AT的相互转换；引起DNA交联作用，引发突变	[33]
	羟化剂（羟胺）	引起胞嘧啶脱氨，实现GC→AT的转换	[35]
生物诱变	噬菌体、质粒和DNA转座子	通过碱基取代和DNA链断裂实现碱基的删除、重复和插入	[30]
	原生质体融合	将两个亲株的原生质体进行融合，形成杂合二倍体，使两个亲株发生基因组重组	[31]
	DNA改组	对多个同源序列组成的文库进行随机片段化，再利用PCR使其发生随机的重组，实现多亲本的基因重组	[34]
	基因组重排	先利用传统诱变手段获得多个表型改进的菌株，然后模拟DNA改组的反应条件，对原生质体进行多次递推式融合，实现正向突变的富集	[26]
复合诱变		结合多种诱变方式提高诱变效率	[30]
新型诱变技术	离子注入诱变	离子注入细胞导致DNA损伤，细胞修复损伤的过程中出现突变	[30]
新型诱变技术	等离子体诱变	等离子体作用于细胞造成DNA损伤，细胞修复损伤的过程中出现突变	[27，30]

分类	诱变技术/诱变剂	描述	参考文献
物理诱变	电离辐射（X射线、γ射线等）	引起DNA双链或单链断裂，实现DNA的删除或结构改变	[30]
	非电离辐射（紫外线）	使嘧啶形成二聚体，实现GC的删除、移码突变以及GC→AT的转换	[32]
化学诱变	烷化剂（烷基磺酸盐、芥子气等）	使DNA碱基发生烷化，导致DNA复制时发生配对错误	[32]
	碱基类似物（嘧啶类似物、嘌呤类似物）	与DNA碱基结构类似，在DNA复制时掺入并引发配对错误	[30]
	移码诱变剂（原黄素、吖啶橙等）	与DNA结合导致碱基增添或缺失	[30]
	脱氨剂（亚硝酸）	引起A、C、G碱基的脱氨，实现GC与AT的相互转换；引起DNA交联作用，引发突变	[33]
	羟化剂（羟胺）	引起胞嘧啶脱氨，实现GC→AT的转换	[35]
生物诱变	噬菌体、质粒和DNA转座子	通过碱基取代和DNA链断裂实现碱基的删除、重复和插入	[30]
	原生质体融合	将两个亲株的原生质体进行融合，形成杂合二倍体，使两个亲株发生基因组重组	[31]
	DNA改组	对多个同源序列组成的文库进行随机片段化，再利用PCR使其发生随机的重组，实现多亲本的基因重组	[34]
	基因组重排	先利用传统诱变手段获得多个表型改进的菌株，然后模拟DNA改组的反应条件，对原生质体进行多次递推式融合，实现正向突变的富集	[26]
复合诱变		结合多种诱变方式提高诱变效率	[30]
新型诱变技术	离子注入诱变	离子注入细胞导致DNA损伤，细胞修复损伤的过程中出现突变	[30]
新型诱变技术	等离子体诱变	等离子体作用于细胞造成DNA损伤，细胞修复损伤的过程中出现突变	[27，30]

类别	分析方法	描述	参考文献
动力学分析	ODE&米氏方程	利用常微分方程（ODE）和米氏方程，能够描述胞内代谢物浓度随时间的变化趋势，从而建立代谢网络的动力学模型	[36]
动力学分析	代谢控制分析（MCA）	MCA可用于估算动力学模型中各个通量的控制系数，从而确定代谢途径中需要过表达的目标基因，增加通过途径的通量	[37]
代谢网络分析	代谢通量分析（MFA）	MFA根据胞内代谢物的质量平衡确定线性方程组并求解，能够计算特定培养条件下细胞内的实际代谢通量分布。MFA需要依赖大量实验数据来增加可测量通量的数量，从而计算出不可测量通量向量	[38]
	通量平衡分析（FBA）	FBA可用于确定细胞代谢网络中每个反应的最佳通量，该方法基于凸分析，通过对系统施加最大化（最小化）的目标函数来确定代谢通量矢量	[39-40]
	代谢途径分析（MPA）	MPA可用于识别代谢网络中存在的所有代谢通量向量，该方法仅以化学计量学和反应热力学为约束条件，不需要动力学参数进行计算（例如基元模式分析）	[41-42]
整合了热力学的代谢网络分析	网络嵌入的热力学分析（NET）	以热力学第二定律为依据，可用于检测代谢组学数据和假定的通量方向是否符合热力学一致性	[43]
整合了热力学的代谢网络分析	基于热力学的代谢通量分析（TMFA）	TMFA同时使用热力学方向性约束和质量守恒约束计算代谢通量分布	[44]
计算机应变优化算法	基于约束的重构与分析（COBRA）	COBRA采用基因组规模的计算机模拟，可用于代谢途径预测和优化，从而改善生产速率和产量	[45]
	最小化代谢调节（MOMA）	MOMA用于使野生型菌株和缺失突变体之间代谢通量分布的差别最小化，能够预测基因操作对代谢网络的影响	[46]
	开/关最小化调节（ROOM）	与MOMA的优化目标相同	[47]
	OptKnock	以产品产率为优化目标的基因敲除分析工具	[48]
	生化网络集成计算浏览器（BNICE）	使用广义酶反应规则发现新代谢途径的计算工具	[49]

类别	分析方法	描述	参考文献
动力学分析	ODE&米氏方程	利用常微分方程（ODE）和米氏方程，能够描述胞内代谢物浓度随时间的变化趋势，从而建立代谢网络的动力学模型	[36]
动力学分析	代谢控制分析（MCA）	MCA可用于估算动力学模型中各个通量的控制系数，从而确定代谢途径中需要过表达的目标基因，增加通过途径的通量	[37]
代谢网络分析	代谢通量分析（MFA）	MFA根据胞内代谢物的质量平衡确定线性方程组并求解，能够计算特定培养条件下细胞内的实际代谢通量分布。MFA需要依赖大量实验数据来增加可测量通量的数量，从而计算出不可测量通量向量	[38]
	通量平衡分析（FBA）	FBA可用于确定细胞代谢网络中每个反应的最佳通量，该方法基于凸分析，通过对系统施加最大化（最小化）的目标函数来确定代谢通量矢量	[39-40]
	代谢途径分析（MPA）	MPA可用于识别代谢网络中存在的所有代谢通量向量，该方法仅以化学计量学和反应热力学为约束条件，不需要动力学参数进行计算（例如基元模式分析）	[41-42]
整合了热力学的代谢网络分析	网络嵌入的热力学分析（NET）	以热力学第二定律为依据，可用于检测代谢组学数据和假定的通量方向是否符合热力学一致性	[43]
整合了热力学的代谢网络分析	基于热力学的代谢通量分析（TMFA）	TMFA同时使用热力学方向性约束和质量守恒约束计算代谢通量分布	[44]
计算机应变优化算法	基于约束的重构与分析（COBRA）	COBRA采用基因组规模的计算机模拟，可用于代谢途径预测和优化，从而改善生产速率和产量	[45]
	最小化代谢调节（MOMA）	MOMA用于使野生型菌株和缺失突变体之间代谢通量分布的差别最小化，能够预测基因操作对代谢网络的影响	[46]
	开/关最小化调节（ROOM）	与MOMA的优化目标相同	[47]
	OptKnock	以产品产率为优化目标的基因敲除分析工具	[48]
	生化网络集成计算浏览器（BNICE）	使用广义酶反应规则发现新代谢途径的计算工具	[49]

模型范围	模型名称	菌株	反应/代谢物	参考文献
核心代谢模型	未报道	大肠杆菌	95/94	[54]
	未报道	谷氨酸棒杆菌	未报道	[55]
	未报道	酿酒酵母	70/83	[56]
	未报道	酿酒酵母	78/98	[57]
	未报道	酿酒酵母	37/27	[58]
全基因组代谢模型（GSMM）	iFF708	酿酒酵母	1145/825 +708基因	[53]
	iND750	酿酒酵母	1149/646 +750基因	[59]
	Yeast 4.0	酿酒酵母	1865/1319 +932基因	[60]
	iJO1366	大肠杆菌	2583/1805 +1366基因	[61]
	未报道	谷氨酸棒杆菌	495/408 +411基因	[62]
	未报道	乳酸菌	621/509 +358基因	[63]
	未报道	枯草芽孢杆菌	754/637 +614基因	[64]
全基因组代谢模型 +基因表达水平	T. maritima	海栖热袍菌	17535/18209	[65]
全基因组代谢模型 +基因表达水平	iOL1650-ME	大肠杆菌	76414/56902	[66]
全基因组代谢模型 +蛋白质结构特性	T. maritima	海栖热袍菌	645/503 +478酶结构	[67]
全基因组代谢模型 +蛋白质结构特性	E. coli GEM-PRO	大肠杆菌	2583/1805 +1268酶结构	[68-69]
全基因组代谢模型 +转录调控	iAF1260 PROM	大肠杆菌	2583/1805 +1773转录调控作用	[70]
全基因组代谢模型 +转录调控	iMH805/837	酿酒酵母	1489/972 +805基因+837转录调控作用	[71]
全细胞模型	‘Whole-cell’ M. genitalium	生殖支原体	28个细胞过程子模块 +525基因	[72]