CIESC Journal ›› 2020, Vol. 71 ›› Issue (9): 3979-3994.DOI: 10.11949/0438-1157.20200516
• Reviews and monographs • Previous Articles Next Articles
Lei QIN1(),Jie YU1,Xiaoyu NING1,Wentao SUN1,Chun LI1,2()
Received:
2020-05-08
Revised:
2020-06-27
Online:
2020-09-05
Published:
2020-09-05
Contact:
Chun LI
通讯作者:
李春
作者简介:
秦磊(1987—),男,博士,基金资助:
CLC Number:
Lei QIN, Jie YU, Xiaoyu NING, Wentao SUN, Chun LI. Synthetic biological system construction and green intelligent biological manufacturing[J]. CIESC Journal, 2020, 71(9): 3979-3994.
秦磊, 俞杰, 宁小钰, 孙文涛, 李春. 合成生物系统构建与绿色生物“智”造[J]. 化工学报, 2020, 71(9): 3979-3994.
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Fig.3 Intelligence of enzymes(a) synthesis of various products by enzyme promiscuity[35]; (b) changing properties of enzymes by regulating temperature[36]
Fig.4 Transcription regulatory elements as biosensors(a) rational design of IPP-response element[53]; (b) cold-induced expression system[59]; (c) light-induced and light-repressed expression system inE. coli[64]; (d) light-induced expression system in S. cerevisiae[65]
Fig.5 Autonomous dynamic regulations(a) FPP feedback inhibition and induction increase amorphadiene production[56]; (b) ergosterol feedback inhibition decreases competitive pathway flux[91]; (c) burden induced CRISPRi feedback regulation[92]; (d) stress-driven feedback regulation of anti-stress system[57]; (e) two count pH sensitive kill switch[93]
1 | Dai Z, Nielsen J. Advancing metabolic engineering through systems biology of industrial microorganisms[J]. Current Opinion in Biotechnology, 2015, 36: 8-15. |
2 | Lee J W, Na D, Park J M, et al. Systems metabolic engineering of microorganisms for natural and non-natural chemicals[J]. Nature Chemical Biology, 2012, 8(6): 536. |
3 | Curran K A, Alper H S. Expanding the chemical palate of cells by combining systems biology and metabolic engineering[J]. Metabolic Engineering, 2012, 14(4): 289-297. |
4 | Zhang F, Rodriguez S, Keasling J D. Metabolic engineering of microbial pathways for advanced biofuels production[J]. Current Opinion in Biotechnology, 2011, 22(6): 775-783. |
5 | Mao X, Liu Z, Sun J, et al. Metabolic engineering for the microbial production of marine bioactive compounds[J]. Biotechnology Advances, 2017, 35(8): 1004-1021. |
6 | Pontrelli S, Chiu T Y, Lan E I, et al. Escherichia coli as a host for metabolic engineering[J]. Metabolic Engineering, 2018, 50: 16-46. |
7 | Matsumoto T, Tanaka T, Kondo A. Engineering metabolic pathways in Escherichia coli for constructing a “microbial chassis” for biochemical production[J]. Bioresource Technology, 2017, 245: 1362-1368. |
8 | Heider S A E, Wendisch V F. Engineering microbial cell factories: metabolic engineering of Corynebacterium glutamicum with a focus on non-natural products[J]. Biotechnology Journal, 2015, 10(8): 1170-1184. |
9 | Becker J, Wittmann C. Bio-based production of chemicals, materials and fuels—Corynebacterium glutamicum as versatile cell factory[J]. Current Opinion in Biotechnology, 2012, 23(4): 631-640. |
10 | Zhang Y, Nielsen J, Liu Z. Engineering yeast metabolism for production of terpenoids for use as perfume ingredients, pharmaceuticals and biofuels[J]. FEMS Yeast Research, 2017, 17: 8. |
11 | Bond C M, Tang Y. Engineering Saccharomyces cerevisiae for production of simvastatin[J]. Metabolic Engineering, 2019, 51: 1-8. |
12 | Yaguchi A, Spagnuolo M, Blenner M. Engineering yeast for utilization of alternative feedstocks[J]. Current Opinion in Biotechnology, 2018, 53: 122-129. |
13 | Ko J K, Lee S M. Advances in cellulosic conversion to fuels: engineering yeasts for cellulosic bioethanol and biodiesel production[J]. Current Opinion in Biotechnology, 2018, 50: 72-80. |
14 | Zhu M, Wang C, Sun W, et al. Boosting 11-oxo-β-amyrin and glycyrrhetinic acid synthesis in Saccharomyces cerevisiaevia pairing novel oxidation and reduction system from legume plants[J]. Metabolic Engineering, 2018, 45: 43-50. |
15 | Biggs B W, De Paepe B, Santos C N S, et al. Multivariate modular metabolic engineering for pathway and strain optimization[J]. Current Opinion in Biotechnology, 2014, 29: 156-162. |
16 | Caspeta L, Castillo T, Nielsen J. Modifying yeast tolerance to inhibitory conditions of ethanol production processes[J]. Frontiers in Bioengineering and Biotechnology, 2015, 3: 184. |
17 | Cunha J T, Romaní A, Costa C E, et al. Molecular and physiological basis of Saccharomyces cerevisiae tolerance to adverse lignocellulose-based process conditions[J]. Applied Microbiology and Biotechnology, 2019, 103: 159-175. |
18 | Xu K, Lv B, Huo Y X, et al. Toward the lowest energy consumption and emission in biofuel production: combination of ideal reactors and robust hosts[J]. Current Opinion in Biotechnology, 2017, 50: 19-24. |
19 | Deparis Q, Claes A, Foulquié-Moreno M R, et al. Engineering tolerance to industrially relevant stress factors in yeast cell factories[J]. FEMS Yeast Research, 2017, 17: 4. |
20 | Morano K A, Grant C M, Moye-Rowley W S. The response to heat shock and oxidative stress in Saccharomyces cerevisiae[J]. Genetics, 2012, 190(4): 1157-1195. |
21 | Xu K, Lee Y S, Li J, et al. Resistance mechanisms and reprogramming of microorganisms for efficient biorefinery under multiple environmental stresses[J]. Synthetic and Systems Biotechnology, 2019, 4(2): 92-98. |
22 | Xu K, Qin L, Bai W, et al. Multilevel defense system (MDS) relieves multiple stresses for economically boosting ethanol production of industrial Saccharomyces cerevisiae[J]. ACS Energy Letters, 2020, 5(2): 572-582. |
23 | Leman J K, Weitzner B D, Lewis S M, et al. Macromolecular modeling and design in Rosetta: recent methods and frameworks[J]. Nature Methods, 2020, 17: 665-680. |
24 | Weitzner B D, Kipnis Y, Daniel A G, et al. A computational method for design of connected catalytic networks in proteins[J]. Protein Science, 2019, 28(12): 2036-2041. |
25 | Chen Z, Boyken S E, Jia M, et al. Programmable design of orthogonal protein heterodimers[J]. Nature, 2019, 565: 106-111. |
26 | Boyken S E, Benhaim M A, Busch F, et al. De novo design of tunable, pH-driven conformational changes[J]. Science, 2019, 364: 658-664. |
27 | Zhang Y, Bartz R. Grigoryan G,et al. Computational design and experimental characterization of peptides intended for pH-dependent membrane insertion and pore formation[J]. ACS Chemical Biology, 2015, 10(4): 1082-1093. |
28 | Kisovec M, Rezelj S, Knap P, et al. Engineering a pH responsive pore forming protein[J]. Scientific Reports, 2017, 7: 42231. |
29 | Omersa N, Aden S, Kisovec M, et al. Design of protein logic gate system operating on lipid membranes[J]. ACS Synthetic Biology, 2020, 9: 316-328. |
30 | Langan R A, Boyken S E, Ng A H, et al. De novo design of bioactive protein switches[J]. Nature, 2019, 572: 205-210. |
31 | Ng A H, Nguyen T H, Gómez-Schiavon M, et al. Modular and tunable biological feedback control using a de novo protein switch[J]. Nature, 2019, 572: 265-269. |
32 | Chen Z, Kibler R D, Hunt A, et al. De novo design of protein logic gates[J]. Science, 2020, 368: 78-84. |
33 | Tinberg C E, Khare S D, Dou J, et al. Computational design of ligand-binding proteins with high affinity and selectivity[J]. Nature, 2013, 501(7466): 212-216. |
34 | Urlacher V B, Girhard M. Cytochrome P450 monooxygenases in biotechnology and synthetic biology[J]. Trends in Biotechnology, 2019, 37: 882. |
35 | Sun W, Xue H, Liu H, et al. Controlling chemo- and regioselectivity of a plant P450 in yeast cell toward rare licorice triterpenoid biosynthesis[J]. ACS Catalysis, 2020, 10: 4253-4260. |
36 | Inda M E, Vandenbranden M, Fernández A, et al. A lipid-mediated conformational switch modulates the thermosensing activity of DesK[J]. Proceedings of the National Academy of Sciences of the USA, 2014, 111(9): 3579-3584. |
37 | Wu M Y, Sung L Y, Li H, et al. Combining CRISPR and CRISPRi systems for metabolic engineering of E. coli and 1,4-BDO biosynthesis[J]. ACS Synthetic Biology, 2017, 6: 2350-2361. |
38 | Rodrigues A L, Becker J, de Souza Lima A O, et al. Systems metabolic engineering of Escherichia coli for gram scale production of the antitumor drug deoxyviolacein from glycerol[J]. Biotechnology and Bioengineering, 2014, 111: 2280-2289. |
39 | Wang S, Hou Y, Chen X, et al. Kick-starting evolution efficiency with an autonomous evolution mutation system[J]. Metabolic Engineering, 2019, 54: 127-136. |
40 | Xie W, Ye L, Lv X, et al. Sequential control of biosynthetic pathways for balanced utilization of metabolic intermediates in Saccharomyces cerevisiae[J]. Metabolic Engineering, 2015, 28: 8-18. |
41 | 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: 528-532. |
42 | Šeputiene V, Motiejūnas D, Suziedelis K, et al. Molecular characterization of the acid-inducible asr gene of Escherichia coli and its role in acid stress response[J]. Journal of Bacteriology, 2003, 185(8): 2475-2484. |
43 | Ogasawara H, Hasegawa A, Kanda E, et al. Genomic SELEX search for target promoters under the control of the PhoQP-RstBA signal relay cascade[J]. Journal of Bacteriology, 2007, 189(13): 4791-4799. |
44 | Hoynes-O’Connor A, Shopera T, Hinman K, et al. Enabling complex genetic circuits to respond to extrinsic environmental signals[J]. Biotechnology and Bioengineering, 2017, 114: 1626-1631. |
45 | Haneburger I, Fritz G, Jurkschat N, et al. Deactivation of the E. coli pH stress sensor CadC by cadaverine[J]. Journal of Molecular Biology, 2012, 424: 15-27. |
46 | Moser F, Borujeni A E, Ghodasara A N, et al. Dynamic control of endogenous metabolism with combinatorial logic circuits[J]. Molecular Systems Biology, 2018, 14: 8605. |
47 | Xiong L, Zeng Y, Tang R Q, et al. Condition specific promoter activities in Saccharomyces cerevisiae[J]. Microbial Cell Factories, 2018, 17: 58. |
48 | Rajkumar A S, Liu G d, Bergenholm D, et al. Engineering of synthetic, stress-responsive yeast promoters[J]. Nucleic Acids Research, 2016, 44(17): 136. |
49 | 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: 1213. |
50 | Xu P, Wang W, Li L, et al. Design and kinetic analysis of a hybrid promoter-regulator system for malonyl-CoA sensing in Escherichia coli[J]. ACS Chemical Biology, 2014, 9(2): 451-458. |
51 | Xu P, Li L, Zhang F, et al. Improving fatty acids production by engineering dynamic pathway regulation and metabolic control[J]. Proceedings of the National Academy of Sciences of the USA, 2014, 111(31): 11299-11304. |
52 | Liang C, Zhang X, Wu J, et al. Dynamic control of toxic natural product biosynthesis by an artificial regulatory circuit[J]. Metabolic Engineering, 2020, 57: 239-246. |
53 | Chou H H, Keasling J D. Programming adaptive control to evolve increased metabolite production[J]. Nature Communications, 2013, 4: 2595. |
54 | Feng J, Jester B W, Tinberg C E, et al. A general strategy to construct small molecule biosensors in eukaryotes[J]. Elife, 2015, 4: e10606. |
55 | Jester B W, Tinberg C E, Rich M S, et al. Engineered biosensors from dimeric ligand-binding domains[J]. ACS Synthetic Biology, 2018, 7: 2457-2467. |
56 | Dahl R H, Zhang F, Alonso-Gutierrez J, et al. Engineering dynamic pathway regulation using stress-response promoters[J]. Nature Biotechnology, 2013, 31(11): 1039-1046. |
57 | Qin L, Dong S, Yu J, et al. Stress-driven dynamic regulation of multiple tolerance genes improves robustness and productive capacity of Saccharomyces cerevisiae in industrial lignocellulose fermentation[J]. Metabolic Engineering, 2020, 61:160-170. |
58 | Valdez-Cruz N A, Caspeta L, Pérez N O, et al. Production of recombinant proteins in E. coli by the heat inducible expression system based on the phage lambda pL and/or pR promoters[J]. Microbial Cell Factories, 2010, 9: 18. |
59 | Zheng Y, Meng F, Zhu Z, et al. A tight cold-inducible switch built by coupling thermosensitive transcriptional and proteolytic regulatory parts[J]. Nucleic Acids Research, 2019, 47(21): 137. |
60 | Guan C R, Cui W J, Cheng J T, et al. Construction and development of an auto-regulatory gene expression system in Bacillus subtilis[J]. Microbial Cell Factories, 2015, 14: 150. |
61 | Fuqua C, Parsek M R, Greenberg E P. Regulation of gene expression by cell-to-cell communication: acylhomoserine lactone quorum sensing[J]. Annual Review of Genetics, 2001, 35: 439-468. |
62 | Jia H, Sun X, Sun H, et al. Intelligent microbial heat-regulating engine (IMHeRE) for improved thermo-robustness and efficiency of bioconversion[J]. ACS Synthetic Biology, 2016, 5: 312-320. |
63 | Zoltowski B D, Motta-Mena L B, Gardner K H. Blue light-induced dimerization of a bacterial LOV-HTH DNA-binding protein[J]. Biochemistry, 2013, 52: 6653-6661. |
64 | Jayaraman P, Devarajan K, Chua T K, et al. Blue light-mediated transcriptional activation and repression of gene expression in bacteria[J]. Nucleic Acids Research, 2016, 44: 6994-7005. |
65 | Zhao E M, Zhang Y, Mehl J, et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production[J]. Nature, 2018, 555: 683. |
66 | Hochrein L, Machens F, Messerschmidt K, et al. PhiReX: a programmable and red light-regulated protein expression switch for yeast[J]. Nucleic Acids Research, 2017, 110: 21130-21135. |
67 | Schmidl S R, Sheth R U, Wu A, et al. Refactoring and optimization of light-switchable Escherichia coli two component systems[J]. ACS Synthetic Biology, 2014, 3: 820-831. |
68 | Olson E J, Hartsough L A, Landry B P, et al. Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals[J]. Nature Methods, 2014, 11: 449-455. |
69 | Neupert J, Karcher D, Bock R. Design of simple synthetic RNA thermometers for temperature-controlled gene expression in Escherichia coli[J]. Nucleic Acids Research, 2008, 36: 124. |
70 | Giuliodori A M, Di Pietro F, Marzi S, et al. The cspA mRNA is a thermosensor that modulates translation of the cold-shock protein CspA[J]. Molecular Cell, 2010, 37(1): 21-33. |
71 | Nechooshtan G, Elgrably-Weiss M, Sheaffer A, et al. A pH-responsive riboregulatory[J]. Genes and Development, 2009, 23: 2650-2662. |
72 | Nechooshtan G, Elgrably-Weiss M, Altuvia S. Changes in transcriptional pausing modify the folding dynamics of the pH-responsive RNA element[J]. Nucleic Acids Research, 2014, 42: 622-630. |
73 | Pham H L, Wong A, Chua N, et al. Engineering a riboswitch-based genetic platform for the self-directed evolution of acid-tolerant phenotypes[J]. Nature Communications, 2017, 8: 411. |
74 | Lukyanov K A, Belousov V V. Genetically encoded fluorescent redox sensors[J]. Biochimica et Biophysica Acta, 2014, 1840(2): 745-756. |
75 | Bilan D S, Belousov V V. New tools for redox biology: from imaging to manipulation[J]. Free Radical Biology and Medicine, 2017, 109: 167-188. |
76 | 俞杰, 秦磊, 许可, 等. 细胞工厂氧化还原状态的荧光探针检测与调控[J].生物加工过程, 2020, 18(1): 60-69. |
Yu J, Qin L, Xu K, et al. Detection and regulation of the redox state in cell factories by fluorescent probes[J]. Chinese Journal of Bioprocess Engineering, 2020, 18(1): 60-69. | |
77 | Bugaj L J, Choksi A T, Mesuda C K, et al. Optogenetic protein clustering and signaling activation in mammalian cells[J]. Nature Methods, 2013, 10: 249-252. |
78 | Taslimi A, Vrana J D, Chen D, et al. An optimized optogenetic clustering tool for probing protein interaction and function[J]. Nature Communications, 2014, 5: 4925. |
79 | Shin Y, Berry J, Pannucci N, et al. Spatiotemporal control of intracellular phase transitions using light-activated optoDroplets[J]. Cell, 2017, 168: 159-171. |
80 | Zhao E M, Suek N, Wilson M Z, et al. Light-based control of metabolic flux through assembly of synthetic organelles[J]. Nature Chemical Biology, 2019, 15: 589-597. |
81 | Dine E, Gil A A, Uribe G, et al. Protein phase separation provides long-term memory of transient spatial stimuli[J]. Cell Systems, 2018, 6: 655-663. |
82 | Gil A A, Laptenok S P, Iuliano J N, et al. Photoactivation of the BLUF protein PixD probed by the site-specific incorporation of fluorotyrosine residues[J]. Journal of the American Chemical Society, 2017, 139: 14638-14648. |
83 | Kim S K, Han G H, Seong W, et al. CRISPR interference-guided balancing of a biosynthetic mevalonate pathway increases terpenoid production[J]. Metabolic Engineering, 2016, 38: 228-240. |
84 | Cunningham-Bryant D, Sun J, Fernandez B, et al. CRISPR-Cas-mediated chemical control of transcriptional dynamics in yeast[J]. ChemBioChem, 2019, 20(12): 1519-1523. |
85 | Koopal B, Kruis A J, Claassens N J, et al. Incorporation of a synthetic amino acid into dCas9 improves control of gene silencing[J]. ACS Synthetic Biology, 2019, 8(2): 216-222. |
86 | Ni J, Wu Y T, Tao F, et al. A coenzyme-free biocatalyst for the value-added utilization of lignin-derived aromatics[J]. Journal of the American Chemical Society, 2018, 140: 16001-16005. |
87 | Bañares A B, Valdehuesa K N G, Ramos K R M, et al. A pH-responsive genetic sensor for the dynamic regulation of D-xylonic acid accumulation in Escherichia coli[J]. Applied Microbiology and Biotechnology, 2020, 104: 2097-2108. |
88 | Bañares A B, Valdehuesa K N G, Ramos K R M, et al. Discovering a novel D-xylonate-responsive promoter: the PyjhI-driven genetic switch towards better 1,2,4-butanetriol production[J]. Applied Microbiology and Biotechnology, 2019, 103: 8063-8074. |
89 | Anesiadis N, Kobayashi H, Cluett W R, et al. Analysis and design of a genetic circuit for dynamic metabolic engineering[J]. ACS Synthetic Biology, 2013, 2(8): 442-452. |
90 | Kim E M, Woo H M, Tian T, et al. Autonomous control of metabolic state by a quorum sensing (QS)-mediated regulator for bisabolene production in engineered E. coli[J]. Metabolic Engineering, 2017, 44: 325-336. |
91 | Yuan J, Ching C B. Dynamic control of ERG9 expression for improved amorpha-4,11-diene production in Saccharomyces cerevisiae[J]. Microbial Cell Factories, 2015, 14: 38. |
92 | Ceroni F, Boo A, Furini S, et al. Burden-driven feedback control of gene expression[J]. Nature Methods, 2018, 15(5): 387-393. |
93 | Stirling F, Naydich A, Bramante J, et al. Synthetic cassettes for pH-mediated sensing, counting and containment[J]. Cell Reports, 2020, 30(9): 3139. |
94 | Zhao N, Bai Y, Liu C G, et al. Flocculating Zymomonas mobilis is a promising host to be engineered for fuel ethanol production from lignocellulosic biomass[J]. Biotechnology Journal, 2014, 9(3): 362-371. |
95 | Govender P, Domingo J L, Bester M C, et al. Controlled expression of the dominant flocculation genes FLO1, FLO5, and FLO11 in Saccharomyces cerevisiae[J]. Applied and Environmental Microbiology, 2008, 74(19): 6041-6052. |
96 | Li Q, Zhao X Q, Chang A K, et al. Ethanol-induced yeast flocculation directed by the promoter of TPS1 encoding trehalose-6-phosphate synthase 1 for efficient ethanol production[J]. Metabolic Engineering, 2012, 14: 1-8. |
97 | Ling C, Qiao G Q, Shuai B W, et al. Engineering self-flocculating Halomonas campaniensis for wastewaterless open and continuous fermentation[J]. Biotechnology and Bioengineering, 2019, 116: 805-815. |
98 | Heler R, Wright A V, Vucelja M, et al. Mutations in Cas9 enhance the rate of acquisition of viral spacer sequences during the CRISPR-Cas immune response[J]. Molecular Cell, 2017, 65(1): 168-175. |
99 | Jiang W, Oikonomou P, Tavazoie S. Comprehensive genome-wide perturbations via CRISPR adaptation reveal complex genetics of antibiotic sensitivity[J]. Cell, 2020, 180: 1-16. |
100 | Xie Z X, Li B Z, Mitchell L A, et al. ‘Perfect’ designer chromosome V and behavior of a ring derivative[J]. Science, 2017, 355(6329): 4704. |
101 | Wu Y, Li B Z, Zhao M, et al. Bug mapping and fitness testing of chemically synthesized chromosome X[J]. Science, 2017, 355(6329): 4706. |
102 | Dymond J S, Richardson S M, Coombes C E, et al. Synthetic chromosome arms function in yeast and generate phenotypic diversity by design[J]. Nature, 2011, 477: 471-476. |
103 | Jia B, Wu Y, Li B Z, et al. Precise control of SCRaMbLE in synthetic haploid and diploid yeast[J]. Nature Communications, 2018, 9(1): 1933. |
104 | Hochrein L, Mitchell L A, Schulz K, et al. L-SCRaMbLE as a tool for light-controlled Cre-mediated recombination in yeast[J]. Nature Communications, 2018, 9(1): 1931. |
105 | Ma L, Li Y, Chen X, et al. SCRaMbLE generates evolved yeasts with increased alkali tolerance[J]. Microbial Cell Factories, 2019, 18(1): 52. |
106 | Shen M J, Wu Y, Yang K, et al. Heterozygous diploid and interspecies SCRaMbLEing[J]. Nature Communications, 2018, 9(1): 1934. |
107 | Luo Z, Wang L, Wang Y, et al. Identifying and characterizing SCRaMbLEd synthetic yeast using ReSCuES[J]. Nature Communications, 2018, 9(1): 1930. |
108 | Blount B A, Gowers G O F, Ho J C H, et al. Rapid host strain improvement by in vivo rearrangement of a synthetic yeast chromosome[J]. Nature Communications, 2018, 9(1): 1932. |
109 | Wu Y, Zhu R Y, Mitchell L A, et al. In vitro DNA SCRaMbLE[J]. Nature Communications, 2018, 9(1): 1935. |
110 | Wang J, Xie Z X, Ma Y, et al. Ring synthetic chromosome V SCRaMbLE[J]. Nature Communications, 2018, 9(1): 3783. |
111 | Gowers G O F, Chee S M, Bell D, et al. Improved betulinic acid biosynthesis using synthetic yeast chromosome recombination and semi-automated rapid LC-MS screening[J]. Nature Communications, 2020, 11(1): 868. |
112 | Liu W, Luo Z, Wang Y, et al. Rapid pathway prototyping and engineering using in vitro and in vivo synthetic genome SCRaMbLE-in methods[J]. Nature Communications, 2018, 9(1): 1936. |
113 | 田锡炜, 王冠, 张嗣良, 等. 工业生物过程智能控制原理和方法进展[J]. 生物工程学报, 2019, 35(10): 2014-2024. |
Tian X W, Wang G, Zhang S L, et al. Progress in intelligent control of industrial bioprocess[J]. Chinese Journal of Biotechnology, 2019, 35(10): 2014-2024. | |
114 | Chen Y, Wang Z J, Chu J, et al. Significant decrease of broth viscosity and glucose consumption in erythromycin fermentation by dynamic regulation of ammonium sulfate and phosphate[J]. Bioresource Technology, 2013, 134: 173-179. |
115 | Zou X, Xia J Y, Chu J, et al. Real-time fluid dynamics investigation and physiological response for erythromycin fermentation scale-up from 50 L to 132 m3 fermenter[J]. Bioprocess and Biosystems Engineering, 2012, 35(5): 789-800. |
116 | 陈晓春. 啤酒发酵系统温度智能控制[J]. 食品工业, 2019, 40(11): 219-221. |
Chen X C. Intelligent temperature control of beer fermentation system[J]. The Food Industry, 2019, 40(11): 219-221. | |
117 | Wang L, Yuan J, Wu C, et al. Practical algorithm for stochastic optimal control problem about microbial fermentation in batch culture[J]. Optimization Letters, 2019, 13: 527-541. |
118 | Grunberger A, Wiechert W, Kohlheyer D. Single-cell microfluidics: opportunity for bioprocess development[J]. Current Opinion in Biotechnology, 2014, 29: 15-23. |
119 | Chen J, Vestergaard M, Shen J, et al. Droplet-based microfluidics as a future tool for strain improvement in lactic acid bacteria[J]. FEMS Microbiology Letters, 2018, 365: 258. |
120 | Macosko E Z, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets[J]. Cell, 2015, 161: 1202-1214. |
121 | Blattman S B, Jiang W, Oikonomou P, et al. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing[J]. Nature Microbiology, 2020, . |
122 | Si T, Chao R, Min Y, et al. Automated multiplex genome-scale engineering in yeast[J]. Nature Communications, 2017, 8: 15187. |
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