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Dynamic Metabolic Control: From the Perspective of Regulation Logic

Synthetic Biology and Engineering. 2023, 1(2), 10012; https://doi.org/10.35534/sbe.2023.10012
Tian Jiang    Chenyi Li    Yuxi Teng    Jianli Zhang    Diana Alexis Logan    Yajun Yan *   
School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
*
Authors to whom correspondence should be addressed.

Received: 20 Jun 2023    Accepted: 07 Aug 2023    Published: 28 Aug 2023   

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Abstract

Establishing microbial cell factories has become a sustainable and increasingly promising approach for the synthesis of valuable chemicals. However, introducing heterologous pathways into these cell factories can disrupt the endogenous cellular metabolism, leading to suboptimal production performance. To address this challenge, dynamic pathway regulation has been developed and proven effective in improving microbial biosynthesis. In this review, we summarized typical dynamic regulation strategies based on their control logic. The applicable scenarios for each control logic were highlighted and perspectives for future research direction in this area were discussed.

Keywords

Dynamic regulation; Control logic; Feedback control; Oscillation

References

1.
Jiang W, Hernandez Villamor D, Peng H, Chen J, Liu L, Haritos V, et al. Metabolic engineering strategies to enable microbial utilization of C1 feedstocks.  Nat. Chem. Biol. 2021, 17, 845–855. [Google Scholar]
2.
Jang WD, Kim GB, Kim Y, Lee SY. Applications of artificial intelligence to enzyme and pathway design for metababolic enginnering.  Curr. Opin. Biotechnol. 2022, 73, 101–107. [Google Scholar]
3.
Zhang J, Hansen LG, Gudich O, Viehrig K, Lassen LMM, Schrubbers L, et al. A microbial supply chain for production of the anti-cancer drug vinblastine.  Nature 2022, 609, 341–347. [Google Scholar]
4.
Qin J, Krivoruchko A, Ji B, Chen Y, Kristensen M, Özdemir E, et al. Engineering yeast metabolism for the discovery and production of polyamines and polyamine analogues.  Nat. Catal. 2021, 4, 498–509. [Google Scholar]
5.
Shen X, Wang J, Wang J, Chen Z, Yuan Q, Yan Y. High-level de novo biosynthesis of arbutin in engineered Escherichia coli.  Metab. Eng. 2017, 42, 52–58. [Google Scholar]
6.
Wang J, Jiang T, Milligan S, Zhang J, Li C, Yan Y. Improving isoprenol production via systematic CRISPRi screening in engineered Escherichia coli.  Green Chem. 2022, 24, 6955–6964. [Google Scholar]
7.
Liu Q, Yu T, Li X, Chen Y, Campbell K, Nielsen J, et al. Rewiring carbon metabolism in yeast for high level production of aromatic chemicals.  Nat. Commun. 2019, 10, 4976. [Google Scholar]
8.
Zhu M, Wang C, Sun W, Zhou A, Wang Y, Zhang G, et al. Boosting 11-oxo-beta-amyrin and glycyrrhetinic acid synthesis in Saccharomyces cerevisiae via pairing novel oxidation and reduction system from legume plants.  Metab. Eng. 2018, 45, 43–50. [Google Scholar]
9.
Yu S, Zheng B, Chen Z, Huo Y-X. Metabolic engineering of Corynebacterium glutamicum for producing branched chain amino acids. Microbial.  Cell Fact. 2021, 20, 1–14. [Google Scholar]
10.
Li X, Zhou Z, Li W, Yan Y, Shen X, Wang J, et al. Design of stable and self-regulated microbial consortia for chemical synthesis.  Nat. Commun. 2022, 13, 1–9. [Google Scholar]
11.
Li C, Zhang R, Wang J, Wilson LM, Yan YJ. Protein Engineering for Improving and Diversifying Natural Product Biosynthesis.  Trends Biotechnol. 2020, 38, 729–744. [Google Scholar]
12.
Pfleger BF, Pitera DJ, Smolke CD, Keasling JD. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes.  Nat. Biotechnol. 2006, 24, 1027–1032. [Google Scholar]
13.
Salis HM, Mirsky EA, Voigt CA. Automated design of synthetic ribosome binding sites to control protein expression.  Nat. Biotechnol. 2009, 27, 946–950. [Google Scholar]
14.
Alper H, Miyaoku K, Stephanopoulos G. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets.  Nat. Biotechnol. 2005, 23, 612–616. [Google Scholar]
15.
Biggs BW, De Paepe B, Santos CNS, De Mey M, Ajikumar PK. Multivariate modular metabolic engineering for pathway and strain optimization.  Curr. Opin. Biotechnol. 2014, 29, 156–162. [Google Scholar]
16.
Li C, Jiang T, Li M, Zou Y, Yan Y. Fine-tuning gene expression for improved biosynthesis of natural products: From transcriptional to post-translational regulation.  Biotechnol. Adv. 2022, 54, 107853. [Google Scholar]
17.
Wang J, Li C, Jiang T, Yan Y. Biosensor-assisted titratable CRISPRi high-throughput (BATCH) screening for over-production phenotypes.  Metab. Eng. 2022, 75, 58–67. [Google Scholar]
18.
Jiang T, Li C, Teng Y, Zhang R, Yan Y. Recent advances in improving metabolic robustness of microbial cell factories.  Curr. Opin. Biotechnol. 2020, 66, 69–77. [Google Scholar]
19.
Lin Y, Shen X, Yuan Q, Yan Y. Microbial biosynthesis of the anticoagulant precursor 4-hydroxycoumarin.  Nat. Commun. 2013, 4, 2603. [Google Scholar]
20.
Wang J, Zhang R, Zhang Y, Yang Y, Lin Y, Yan Y. Developing a pyruvate-driven metabolic scenario for growth-coupled microbial production.  Metab. Eng. 2019, 55, 191–200. [Google Scholar]
21.
Wang J, Teng Y, Zhang R, Wu Y, Lou L, Zou Y, et al. Engineering a PAM-flexible SpdCas9 variant as a universal gene repressor.  Nat. Commun. 2021, 12, 6916. [Google Scholar]
22.
Zhang R, Yang Y, Wang J, Lin Y, Yan Y. Synthetic symbiosis combining plasmid displacement enables rapid construction of phenotype-stable strains.  Metab. Eng. 2019, 55, 85–91. [Google Scholar]
23.
Dahl RH, Zhang F, Alonso-Gutierrez J, Baidoo E, Batth TS, Redding-Johanson AM, et al. Engineering dynamic pathway regulation using stress-response promoters.  Nat. Biotechnol. 2013, 31, 1039–1046. [Google Scholar]
24.
Zaslaver A, Mayo AE, Rosenberg R, Bashkin P, Sberro H, Tsalyuk M, et al. Just-in-time transcription program in metabolic pathways.  Nat. Genet. 2004, 36, 486–491. [Google Scholar]
25.
Farmer WR, Liao JC. Improving lycopene production in Escherichia coli by engineering metabolic control.  Nat. Biotechnol. 2000, 18, 533–537. [Google Scholar]
26.
Gao C, Wang S, Hu G, Guo L, Chen X, Xu P, et al. Engineering Escherichia coli for malate production by integrating modular pathway characterization with CRISPRi‐guided multiplexed metabolic tuning.  Biotechnol. Bioeng. 2018, 115, 661–672. [Google Scholar]
27.
Cress BF, Leitz QD, Kim DC, Amore TD, Suzuki JY, Linhardt RJ, et al. CRISPRi-mediated Metabolic engineering of E. coli for O-methylated anthocyanin production.  Microb. Cell Fact. 2017, 16, 1–14. [Google Scholar]
28.
Soma Y, Tsuruno K, Wada M, Yokota A, Hanai T. Metabolic flux redirection from a central metabolic pathway toward a synthetic pathway using a metabolic toggle switch.  Metab. Eng. 2014, 23, 175–184. [Google Scholar]
29.
Wu M-Y, Sung L-Y, Li H, Huang C-H, Hu Y-C. Combining CRISPR and CRISPRi systems for Metabolic engineering of E. coli and 1, 4-BDO biosynthesis.  ACS Synth. Biol. 2017, 6, 2350–2361. [Google Scholar]
30.
Reizman IM, Stenger AR, Reisch CR, Gupta A, Connors NC, Prather KL. Improvement of glucaric acid production in E. coli via dynamic control of metabolic fluxes.  Metab. Eng. Commun. 2015, 2, 109–116. [Google Scholar]
31.
Brockman IM, Prather KL. Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites.  Metab. Eng. 2015, 28, 104–113. [Google Scholar]
32.
Xie W, Ye L, Lv X, Xu H, Yu H. Sequential control of biosynthetic pathways for balanced utilization of metabolic intermediates in Saccharomyces cerevisiae.  Metab. Eng. 2015, 28, 8–18. [Google Scholar]
33.
Rajkumar AS, Liu G, Bergenholm D, Arsovska D, Kristensen M, Nielsen J, et al. Engineering of synthetic, stress-responsive yeast promoters.  Nucleic Acids Res. 2016, 44, e136. [Google Scholar]
34.
Sun J, Tian K, Wang J, Dong Z, Liu X, Permaul K, et al. Improved ethanol productivity from lignocellulosic hydrolysates by Escherichia coli with regulated glucose utilization.  Microb Cell Fact. 2018, 17, 66. [Google Scholar]
35.
Fang Y, Wang J, Ma W, Yang J, Zhang H, Zhao L, et al. Rebalancing microbial carbon distribution for L-threonine maximization using a thermal switch system.  Metab. Eng. 2020, 61, 33–46. [Google Scholar]
36.
Harder BJ, Bettenbrock K, Klamt S. Temperature‐dependent dynamic control of the TCA cycle increases volumetric productivity of itaconic acid production by Escherichia coli.  Biotechnol. Bioeng. 2018, 115, 156–164. [Google Scholar]
37.
Zhao EM, Zhang Y, Mehl J, Park H, Lalwani MA, Toettcher JE, et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production.  Nature 2018, 555, 683–687. [Google Scholar]
38.
Lalwani MA, Ip SS, Carrasco-Lopez C, Day C, Zhao EM, Kawabe H, et al. Optogenetic control of the lac operon for bacterial chemical and protein production.  Nat. Chem. Biol. 2021, 17, 71–79. [Google Scholar]
39.
Wang S, Luo Y, Jiang W, Li X, Qi Q, Liang Q. Development of Optogenetic Dual-Switch System for Rewiring Metabolic Flux for Polyhydroxybutyrate Production.  Molecules 2022, 27, 617. [Google Scholar]
40.
Wu Y, Chen T, Liu Y, Tian R, Lv X, Li J, et al. Design of a programmable biosensor-CRISPRi genetic circuits for dynamic and autonomous dual-control of metabolic flux in Bacillus subtilis.  Nucleic Acids Res. 2020, 48, 996–1009. [Google Scholar]
41.
Wei L, Zhao J, Gao J, Du M, Xu N, Du H, et al. Engineering of Corynebacterium glutamicum for high-level γ-aminobutyric acid production from glycerol by dynamic metabolic control.  Metab. Eng. 2022, 69, 134–146. [Google Scholar]
42.
Li C, Zou Y, Jiang T, Zhang J, Yan Y. Harnessing plasmid replication mechanism to enable dynamic control of gene copy in bacteria.  Metab. Eng. 2022, 70, 67–78. [Google Scholar]
43.
Yang Y, Lin Y, Wang J, Wu Y, Zhang R, Cheng M, et al. Sensor-regulator and RNAi based bifunctional dynamic control network for engineered microbial synthesis.  Nat. Commun. 2018, 9, 3043. [Google Scholar]
44.
Liang C, Zhang X, Wu J, Mu S, Wu Z, Jin JM, et al. Dynamic control of toxic natural product biosynthesis by an artificial regulatory circuit. Metab. Eng. 2020, 57, 239–246. [Google Scholar]
45.
David F, Nielsen J, Siewers V. Flux Control at the Malonyl-CoA Node through Hierarchical Dynamic Pathway Regulation in Saccharomyces cerevisiae.  ACS Synth. Biol. 2016, 5, 224–233. [Google Scholar]
46.
Wang S, Hao R, Jin X, Li X, Qi Q, Liang Q. Dynamic Regulation of Transporter Expression to Increase L-Threonine Production Using L-Threonine Biosensors.  Fermentation 2022, 8, 250. [Google Scholar]
47.
Gupta A, Reizman IM, Reisch CR, Prather KL. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit.  Nat. Biotechnol. 2017, 35, 273–279. [Google Scholar]
48.
Dinh CV, Prather KLJ. Development of an autonomous and bifunctional quorum-sensing circuit for metabolic flux control in engineered Escherichia coli.  Proc. Natl. Acad. Sci. USA 2019, 116, 25562–25568. [Google Scholar]
49.
Ge C, Yu Z, Sheng H, Shen X, Sun X, Zhang Y, et al. Redesigning regulatory components of quorum-sensing system for diverse metabolic control.  Nat. Commun. 2022, 13, 1–12. [Google Scholar]
50.
Yang X, Liu J, Zhang J, Shen Y, Qi Q, Bao X, et al. Quorum sensing-mediated protein degradation for dynamic metabolic pathway control in Saccharomyces cerevisiae.  Metab. Eng. 2021, 64, 85–94. [Google Scholar]
51.
Xu X, Li X, Liu Y, Zhu Y, Li J, Du G, et al. Pyruvate-responsive genetic circuits for dynamic control of central metabolism.  Nat. Chem. Biol. 2020, 16, 1261–1268. [Google Scholar]
52.
Xu P, Li L, Zhang F, Stephanopoulos G, Koffas M. Improving fatty acids production by engineering dynamic pathway regulation and metabolic control.  Proc. Natl. Acad. Sci. USA 2014, 111, 11299–11304. [Google Scholar]
53.
Shen HJ, Cheng BY, Zhang YM, Tang L, Li Z, Bu YF, et al. Dynamic control of the mevalonate pathway expression for improved zeaxanthin production in Escherichia coli and comparative proteome analysis.  Metab. Eng. 2016, 38, 180–190. [Google Scholar]
54.
Jiang T, Li C, Zou Y, Zhang J, Gan Q, Yan Y. Establishing an Autonomous Cascaded Artificial Dynamic (AutoCAD) regulation system for improved pathway performance.  Metab. Eng. 2022, 74, 1–10. [Google Scholar]
55.
Zhou S, Yuan SF, Nair PH, Alper HS, Deng Y, Zhou J. Development of a growth coupled and multi-layered dynamic regulation network balancing malonyl-CoA node to enhance (2S)-naringenin biosynthesis in Escherichia coli.  Metab. Eng. 2021, 67, 41–52. [Google Scholar]
56.
Terpe K. Overview of bacterial expression systems for heterologous protein production: from molecular and biochemical fundamentals to commercial systems.  Appl. Microbiol. Biotechnol. 2006, 72, 211–222. [Google Scholar]
57.
Lalwani MA, Zhao EM, Avalos JL. Current and future modalities of dynamic control in metabolic engineering.  Curr. Opin. Biotechnol. 2018, 52, 56–65. [Google Scholar]
58.
Lu Z, Peng B, Ebert BE, Dumsday G, Vickers CE. Auxin-mediated protein depletion for metabolic engineering in terpene-producing yeast.  Nat. Commun. 2021, 12, 1–13. [Google Scholar]
59.
Paddon CJ, Westfall P, Pitera DJ, Benjamin K, Fisher K, McPhee D, et al. High-level semi-synthetic production of the potent antimalarial artemisinin.  Nature 2013, 496, 528–532. [Google Scholar]
60.
Yin X, Shin H-D, Li J, Du G, Liu L, Chen J. P gas, a low-pH-induced promoter, as a tool for dynamic control of gene expression for Metabolic engineering of Aspergillus niger.  Appl. Environ. Microbiol. 2017, 83, e03222–e03216. [Google Scholar]
61.
Richter F, Fonfara I, Bouazza B, Schumacher CH, Bratovič M, Charpentier E, et al. Engineering of temperature- and light-switchable Cas9 variants.  Nucleic Acids Res. 2013, 44, gkw930. [Google Scholar]
62.
Elvin CM, Thompson PR, Argall ME, Hendr NP, Stamford PJ, Lilley PE, et al. Modified bacteriophage lambda promoter vectors for overproduction of proteins in Escherichia coli.  Gene 1990, 87, 123–126. [Google Scholar]
63.
Love CA, Lilley PE, Dixon NE. Stable high-copy-number bacteriophage λ promoter vectors for overproduction of proteins in Escherichia coli. Gene 1996, 176, 49–53. [Google Scholar]
64.
Wang X, Han JN, Zhang X, Ma YY, Lin Y, Wang H, et al. Reversible thermal regulation for bifunctional dynamic control of gene expression in Escherichia coli.  Nat. Commun. 2021, 12, 1411. [Google Scholar]
65.
Motta-Mena LB, Reade A, Mallory MJ, Glantz S, Weiner OD, Lynch KW, et al. An optogenetic gene expression system with rapid activation and deactivation kinetics.  Nat. Chem. Biol. 2014, 10, 196–202. [Google Scholar]
66.
Hochrein L, Machens F, Messerschmidt K, Mueller-Roeber B. PhiReX: a programmable and red light-regulated protein expression switch for yeast.  Nucleic Acids Res. 2017, 45, 9193–9205. [Google Scholar]
67.
Machens F, Ran G, Ruehmkorff C, Meyer Auf der Heyde J, Mueller-Roeber B, Hochrein L. PhiReX 2.0: A Programmable and Red Light-Regulated CRISPR-dCas9 System for the Activation of Endogenous Genes in Saccharomyces cerevisiae.  ACS Synth. Biol. 2023, 12, 1046–1057. [Google Scholar]
68.
Tandar ST, Senoo S, Toya Y, Shimizu H. Optogenetic switch for controlling the central metabolic flux of Escherichia coli. Metab. Eng. 2019, 55, 68–75. [Google Scholar]
69.
Shen X, Wang J, Li C, Yuan Q, Yan Y. Dynamic gene expression engineering as a tool in pathway engineering.  Curr. Opin. Biotechnol. 2019, 59, 122–129. [Google Scholar]
70.
Zhao EM, Suek N, Wilson MZ, Dine E, Pannucci NL, Gitai Z, et al. Light-based control of metabolic flux through assembly of synthetic organelles.  Nat. Chem. Biol. 2019, 15, 589–597. [Google Scholar]
71.
Siedler S, Stahlhut SG, Malla S, Maury J, Neves AR. Novel biosensors based on flavonoid-responsive transcriptional regulators introduced into Escherichia coli.  Metab. Eng. 2014, 21, 2–8. [Google Scholar]
72.
Yang Y, Lin Y, Li L, Linhardt RJ, Yan Y. Regulating malonyl-CoA metabolism via synthetic antisense RNAs for enhanced biosynthesis of natural products.  Metab. Eng. 2015, 29, 217–226. [Google Scholar]
73.
Jiang T, Li C, Yan Y. Optimization of a p-Coumaric Acid Biosensor System for Versatile Dynamic Performance.  ACS Synth Biol. 2021, 10, 132–144. [Google Scholar]
74.
Brockman IM, Prather KL. Dynamic Metabolic engineering: New strategies for developing responsive cell factories.  Biotechnol. J. 2015, 10, 1360–1369. [Google Scholar]
75.
Mahr R, Frunzke J. Transcription factor-based biosensors in biotechnology: current state and future prospects.  Appl. Microbiol. Biotechnol. 2016, 100, 79–90. [Google Scholar]
76.
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold.  Nature 2021, 596, 583–589. [Google Scholar]
77.
Zou Y, Li C, Zhang R, Jiang T, Liu N, Wang J, et al. Exploring the Tunability and Dynamic Properties of MarR-PmarO Sensor System in Escherichia coli.  ACS Synth. Biol. 2021, 10, 2076–2086. [Google Scholar]
78.
Prescott RD, Decho AW. Flexibility and adaptability of quorum sensing in nature.  Trends Microbiol. 2020, 28, 436–444. [Google Scholar]
79.
Williams TC, Averesch NJH, Winter G, Plan MR, Vickers CE, Nielsen LK, et al. Quorum-sensing linked RNA interference for dynamic metabolic pathway control in Saccharomyces cerevisiae.  Metab. Eng. 2015, 29, 124–134. [Google Scholar]
80.
Diggle SP, Griffin AS, Campbell GS, West SA. Cooperation and conflict in quorum-sensing bacterial populations.  Nature 2007, 450, 411–414. [Google Scholar]
81.
Gu F, Jiang W, Mu Y, Huang H, Su T, Luo Y, et al. Quorum Sensing-Based Dual-Function Switch and Its Application in Solving Two Key Metabolic engineering Problems.  ACS Synth. Biol. 2020, 9, 209–217. [Google Scholar]
82.
Whiteley M, Diggle SP, Greenberg EPJN. Progress in and promise of bacterial quorum sensing research.  Nature 2017, 551, 313–320. [Google Scholar]
83.
Minogue TD, Wehland-von Trebra M, Bernhard F, von Bodman SB. The autoregulatory role of EsaR, a quorum-sensing regulator in Pantoea stewartii ssp. stewartii: evidence for a repressor function.  Mol. Microbiol. 2002, 44, 1625–1635. [Google Scholar]
84.
Tian J, Yang G, Gu Y, Sun X, Lu Y, Jiang W. Developing an endogenous quorum-sensing based CRISPRi circuit for autonomous and tunable dynamic regulation of multiple targets in Streptomyces.  Nucleic Acids Res. 2020, 48, 8188–8202. [Google Scholar]
85.
Doong SJ, Gupta A, Prather KLJ. Layered dynamic regulation for improving metabolic pathway productivity in Escherichia coli.  Proc. Natl. Acad. Sci. USA 2018, 115, 2964–2969. [Google Scholar]
86.
Chen K, Dou J, Tang S, Yang Y, Wang H, Fang H, et al. Deletion of the aroK gene is essential for high shikimic acid accumulation through the shikimate pathway in E. coli.  Bioresour. Technol. 2012, 119, 141–147. [Google Scholar]
87.
Peng L, Arauzo-Bravo MJ, Shimizu K. Metabolic flux analysis for a ppc mutant Escherichia coli based on 13C-labelling experiments together with enzyme activity assays and intracellular metabolite measurements.  FEMS Microbiol. Lett. 2004, 235, 17–23. [Google Scholar]
88.
Rueping M, Nachtsheim BJ. A review of new developments in the Friedel–Crafts alkylation—From green chemistry to asymmetric catalysis.  Beilstein J. Org. Chem. 2010, 6, 6. [Google Scholar]
89.
Shen X, Mahajani M, Wang J, Yang Y, Yuan Q, Yan Y, et al. Elevating 4-hydroxycoumarin production through alleviating thioesterase-mediated salicoyl-CoA degradation.  Metab. Eng. 2017, 42, 59–65. [Google Scholar]
90.
Cui S, Lv X, Wu Y, Li J, Du G, Ledesma-Amaro R, et al. Engineering a Bifunctional Phr60-Rap60-Spo0A Quorum-Sensing Molecular Switch for Dynamic Fine-Tuning of Menaquinone-7 Synthesis in Bacillus subtilis.  ACS Synth. Biol. 2019, 8, 1826–1837. [Google Scholar]
91.
Liu D, Sica MS, Mao J, Chao LF, Siewers V. A p-Coumaroyl-CoA Biosensor for Dynamic Regulation of Naringenin Biosynthesis in Saccharomyces cerevisiae.  ACS Synth. Biol. 2022, 11, 3228–3238. [Google Scholar]
92.
Chu HS, Kim YS, Lee CM, Lee JH, Jung WS, Ahn JH, et al. Metabolic engineering of 3‐hydroxypropionic acid biosynthesis in Escherichia coli. Biotechnol. Bioeng. 2015, 112, 356–364. [Google Scholar]
93.
Zhang F, Carothers JM, Keasling JD. Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids.  Nat. Biotechnol. 2012, 30, 354–359. [Google Scholar]
94.
Wang R, Cress BF, Yang Z, Hordines JC, Zhao S, Jung GY, et al. Design and characterization of biosensors for the screening of modular assembled naringenin biosynthetic library in Saccharomyces cerevisiae.  ACS Synth. Biol. 2019, 8, 2121–2130. [Google Scholar]
95.
Yan Y, Kohli A, Koffas MA. Biosynthesis of natural flavanones in Saccharomyces cerevisiae.  Appl. Environ. Microbiol. 2005, 71, 5610–5613. [Google Scholar]
96.
Ghofrani S, Joghataei MT, Mohseni S, Baluchnejadmojarad T, Bagheri M, Khamse S, et al. Naringenin improves learning and memory in an Alzheimer’s disease rat model: Insights into the underlying mechanisms.  Eur. J. Pharmacol. 2015, 764, 195–201. [Google Scholar]
97.
Xiu Y, Jang S, Jones JA, Zill NA, Linhardt RJ, Yuan Q, et al. Naringenin-responsive riboswitch-based fluorescent biosensor module for Escherichia coli co-cultures.  Biotechnol. Bioeng. 2017, 114, 2235–2244. [Google Scholar]
98.
Fowler ZL, Gikandi WW, Koffas MAG. Increased Malonyl Coenzyme A Biosynthesis by Tuning the Escherichia coli Metabolic Network and Its Application to Flavanone Production.  Appl. Environ. Microbiol. 2009, 75, 5831–5839. [Google Scholar]
99.
Leonard E, Yan Y, Fowler ZL, Li Z, Lim C-G, Lim K-H, et al. Strain Improvement of Recombinant Escherichia coli for Efficient Production of Plant Flavonoids.  Mol. Pharm. 2008, 5, 257–265. [Google Scholar]
100.
Xu P, Ranganathan S, Fowler ZL, Maranas CD, Koffas MA. Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA.  Metab. Eng. 2011, 13, 578–587. [Google Scholar]
101.
Lv Y, Gu Y, Xu J, Zhou J, Xu P. Coupling metabolic addiction with negative autoregulation to improve strain stability and pathway yield.  Metab. Eng. 2020, 61, 79–88. [Google Scholar]
102.
Goelzer A, Bekkal Brikci F, Martin-Verstraete I, Noirot P, Bessières P, Aymerich S, et al. Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis.  BMC Syst. Biol. 2008, 2, 1–18. [Google Scholar]
103.
Cameron DE, Collins JJ. Tunable protein degradation in bacteria.  Nat. Biotechnol. 2014, 32, 1276–1281. [Google Scholar]
104.
Gao C, Hou J, Xu P, Guo L, Chen X, Hu G, et al. Programmable biomolecular switches for rewiring flux in Escherichia coli.  Nat. Commun. 2019, 10, 3751. [Google Scholar]
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