Coupling feedback genetic circuits with growth phenotype for dynamic population control and intelligent bioproduction

Metabolic Engineering

Volumn 54, Issue , 2019, Pages 109-116

  Lv, Yongkun ^a,b   Qian, Shuai ^c   Du, Guocheng ^b   Chen, Jian ^b   Zhou, Jingwen ^b   Xu, Peng ^a  

^a University of Marylandw Baltimore County   (United States)

^b JIANGNAN UNIVERSITY   (China)

^c BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

Dynamic population control; Genetic circuits; Growth fitness; Metabolic engineering; Synthetic biology

Indexed keywords

BIOELECTRIC POTENTIALS; CELL CULTURE; CELL PROLIFERATION; COMPETITION; COST ENGINEERING; FEEDBACK; GENE EXPRESSION; MEDICAL APPLICATIONS; METABOLIC ENGINEERING; METABOLISM; POPULATION STATISTICS; PRODUCT DESIGN; QUALITY CONTROL; ROBUST CONTROL; SYNTHETIC BIOLOGY; TIMING CIRCUITS;

BIOPROCESS ENGINEERING; CELLULAR PERFORMANCE; DYNAMIC POPULATION; GENETIC CIRCUITS; INDUSTRIAL FERMENTATION; MICROBIAL CO-OPERATION; MICROBIAL INTERACTIONS; TRANSCRIPTIONAL FACTORS;

CELL ENGINEERING;

CELL GROWTH; FEEDBACK SYSTEM; METABOLIC ENGINEERING; METABOLIC STRESS; METABOLITE; MICROBIAL INTERACTION; NONHUMAN; PHENOTYPE; POPULATION DYNAMICS; PRIORITY JOURNAL; QUALITY CONTROL; REVIEW; BIOLOGICAL MODEL; BIOTECHNOLOGY; GENE REGULATORY NETWORK;

BIOTECHNOLOGY; GENE REGULATORY NETWORKS; METABOLIC ENGINEERING; MODELS, BIOLOGICAL;

EID: 85063669400     PISSN: 10967176     EISSN: 10967184     Source Type: Journal    
DOI: 10.1016/j.ymben.2019.03.009     Document Type: Review

References (43)

1. Binder, D., Drepper, T., Jaeger, K.E., Delvigne, F., Wiechert, W., Kohlheyer, D., Grunberger, A., Homogenizing bacterial cell factories: analysis and engineering of phenotypic heterogeneity. Metab. Eng. 42 (2017), 145–156.
2. Brockman, I.M., Prather, K.L.J., Dynamic knockdown of E. coli central metabolism for redirecting fluxes of primary metabolites. Metab. Eng. 28 (2015), 104–113.
3. Ceroni, F., Algar, R., Stan, G.B., Ellis, T., Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat. Methods 12 (2015), 415–418.
4. Ceroni, F., Boo, A., Furini, S., Gorochowski, T.E., Borkowski, O., Ladak, Y.N., Awan, A.R., Gilbert, C., Stan, G.B., Ellis, T., Burden-driven feedback control of gene expression. Nat. Methods 15 (2018), 387–393.
5. Doong, S.J., Gupta, A., Prather, K.L.J., Layered dynamic regulation for improving metabolic pathway productivity in Escherichia coli. Proc. Natl. Acad. Sci. U. S. A. 115 (2018), 2964–2969.
6. Dunlop, M.J., Keasling, J.D., Mukhopadhyay, A., A model for improving microbial biofuel production using a synthetic feedback loop. Syst. Synth. Biol. 4 (2010), 95–104.
7. Enrico Bena, C., Grob, A., Isalan, M., Bosia, C., Ceroni, F., Commentary: synthetic addiction extends the productive life time of engineered Escherichia coli populations. Front. Bioeng. Biotechnol., 6, 2018, 77.
8. Glick, B.R., Metabolic load and heterologous gene expression. Biotechnol. Adv. 13 (1995), 247–261.
9. Gohil, N., Panchasara, H., Patel, S., Ramírez-García, R., Singh, V., Book review: recent advances in yeast metabolic engineering. Front. Bioeng. Biotechnol., 5, 2017, 71.
10. Gupta, A., Reizman, I.M.B., Reisch, C.R., Prather, K.L.J., Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit. Nat. Biotechnol., 35, 2017, 273.
11. Holtz, W.J., Keasling, J.D., Engineering static and dynamic control of synthetic pathways. Cell 140 (2010), 19–23.
12. Kang, C.W., Lim, H.G., Yang, J., Noh, M.H., Seo, S.W., Jung, G.Y., Synthetic auxotrophs for stable and tunable maintenance of plasmid copy number. Metab. Eng. 48 (2018), 121–128.
13. Lan, E.I., Liao, J.C., ATP drives direct photosynthetic production of 1-butanol in cyanobacteria. Proc. Natl. Acad. Sci. U. S. A. 109 (2012), 6018–6023.
14. Lee, J.W., Kim, T.Y., Jang, Y.S., Choi, S., Lee, S.Y., Systems metabolic engineering for chemicals and materials. Trends Biotechnol. 29 (2011), 370–378.
15. Leonard, E., Ajikumar, P.K., Thayer, K., Xiao, W.H., Mo, J.D., Tidor, B., Stephanopoulos, G., Prather, K.L., Combining metabolic and protein engineering of a terpenoid biosynthetic pathway for overproduction and selectivity control. Proc. Natl. Acad. Sci. U. S. A. 107 (2010), 13654–13659.
16. Lin, Y., Shen, X., Yuan, Q., Yan, Y., Microbial biosynthesis of the anticoagulant precursor 4-hydroxycoumarin. Nat. Commun., 4, 2013, 2603.
17. Liu, S.D., Wu, Y.N., Wang, T.M., Zhang, C., Xing, X.H., Maltose utilization as a novel selection strategy for continuous evolution of microbes with enhanced metabolite production. ACS Synth. Biol. 6 (2017), 2326–2338.
18. Lynch, M., Marinov, G.K., The bioenergetic costs of a gene. Proc. Natl. Acad. Sci. U. S. A. 112 (2015), 15690–15695.
19. Minogue, T.D., Trebra, M. W.-v., Bernhard, F., Bodman, S. B. v., The autoregulatory role of EsaR, a quorum-sensing regulator in Pantoea stewartii ssp. stewartii: evidence for a repressor function. Mol. Microbiol. 44 (2002), 1625–1635.
20. Moon, T.S., Yoon, S.H., Lanza, A.M., Roy-Mayhew, J.D., Prather, K.L., Production of glucaric acid from a synthetic pathway in recombinant Escherichia coli. Appl. Environ. Microbiol. 75 (2009), 589–595.
21. Moser, F., Broers, N.J., Hartmans, S., Tamsir, A., Kerkman, R., Roubos, J.A., Bovenberg, R., Voigt, C.A., Genetic circuit performance under conditions relevant for industrial bioreactors. ACS Synth. Biol. 1 (2012), 555–564.
22. Nielsen, J., Fussenegger, M., Keasling, J., Lee, S.Y., Liao, J.C., Prather, K., Palsson, B., Engineering synergy in biotechnology. Nat. Chem. Biol. 10 (2014), 319–322.
23. Papagianni, M., Recent advances in engineering the central carbon metabolism of industrially important bacteria. Microb. Cell Factories, 11, 2012, 50.
24. Qiao, K., Imam Abidi, S.H., Liu, H., Zhang, H., Chakraborty, S., Watson, N., Kumaran Ajikumar, P., Stephanopoulos, G., Engineering lipid overproduction in the oleaginous yeast Yarrowia lipolytica. Metab. Eng. 29 (2015), 56–65.
25. Rugbjerg, P., Myling-Petersen, N., Porse, A., Sarup-Lytzen, K., Sommer, M.O.A., Diverse genetic error modes constrain large-scale bio-based production. Nat. Commun., 9, 2018, 787.
26. Rugbjerg, P., Sarup-Lytzen, K., Nagy, M., Sommer, M.O.A., Synthetic addiction extends the productive life time of engineered Escherichia coli populations. Proc. Natl. Acad. Sci. U. S. A. 115 (2018), 2347–2352.
27. Schmitz, A.C., Hartline, C.J., Zhang, F., Engineering microbial metabolite dynamics and heterogeneity. Biotechnol. J., 12, 2017.
28. Singh, A., Cher Soh, K., Hatzimanikatis, V., Gill, R.T., Manipulating redox and ATP balancing for improved production of succinate in E. coli. Metab. Eng. 13 (2011), 76–81.
29. Snoek, T., Romero-Suarez, D., Zhang, J., Ambri, F., Skjoedt, M.L., Sudarsan, S., Jensen, M.K., Keasling, J.D., An orthogonal and pH-tunable sensor-selector for muconic acid biosynthesis in yeast. ACS Synth. Biol. 7 (2018), 995–1003.
30. Stephanopoulos, G., Synthetic biology and metabolic engineering. ACS Synth. Biol. 1 (2012), 514–525.
31. Tai, M., Stephanopoulos, G., Engineering the push and pull of lipid biosynthesis in oleaginous yeast Yarrowia lipolytica for biofuel production. Metab. Eng. 15 (2013), 1–9.
32. Tai, Y.S., Xiong, M.Y., Zhang, K.C., Engineered biosynthesis of medium-chain esters in Escherichia coli. Metab. Eng. 27 (2015), 20–28.
33. Tang, S.Y., Cirino, P.C., Design and application of a mevalonate-responsive regulatory protein. Angew. Chem. Int. Ed. 50 (2011), 1084–1086.
34. Taniguchi, Y., Choi, P.J., Li, G.W., Chen, H.Y., Babu, M., Hearn, J., Emili, A., Xie, X.S., Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329 (2010), 533–538.
35. Thodey, K., Galanie, S., Smolke, C.D., A microbial biomanufacturing platform for natural and semisynthetic opioids. Nat. Chem. Biol. 10 (2014), 837–844.
36. Woolston, B.M., Edgar, S., Stephanopoulos, G., Metabolic engineering: past and future. Annu. Rev. Chem. Biomol. Eng. 4 (2013), 259–288.
37. Wu, G., Yan, Q., Jones, J.A., Tang, Y.J., Fong, S.S., Koffas, M.A.G., Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol. 34 (2016), 652–664.
38. Xiao, Y., Bowen, C.H., Liu, D., Zhang, F.Z., Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis. Nat. Chem. Biol. 12 (2016), 339–344.
39. Xiong, M.Y., Schneiderman, D.K., Bates, F.S., Hillmyer, M.A., Zhang, K.C., Scalable production of mechanically tunable block polymers from sugar. Proc. Natl. Acad. Sci. U. S. A. 111 (2014), 8357–8362.
40. Xu, P., Li, L.Y., Zhang, F.M., Stephanopoulos, G., Koffas, M., Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. Proc. Natl. Acad. Sci. U. S. A. 111 (2014), 11299–11304.
41. Zelcbuch, L., Antonovsky, N., Bar-Even, A., Levin-Karp, A., Barenholz, U., Dayagi, M., Liebermeister, W., Flamholz, A., Noor, E., Amram, S., Brandis, A., Bareia, T., Yofe, I., Jubran, H., Milo, R., Spanning high-dimensional expression space using ribosome-binding site combinatorics. Nucleic Acids Res., 41, 2013, e98.
42. Zhang, F.Z., Carothers, J.M., Keasling, J.D., Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat. Biotechnol. 30 (2012), 354–359.
43. Zuniga, C., Zaramela, L., Zengler, K., Elucidation of complexity and prediction of interactions in microbial communities. Microb. Biotechnol. 10 (2017), 1500–1522.