Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
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Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

  • Lee, SuRin (Department of Biotechnology, the Catholic University of Korea) ;
  • Kim, Pil (Department of Biotechnology, the Catholic University of Korea)
  • Received : 2020.04.01
  • Accepted : 2020.05.03
  • Published : 2020.06.28
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Abstract

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

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References

  1. Phaneuf PV, Gosting D, Palsson BO, Feist AM. 2019. ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation. Nucleic Acids Res. 47: D1164-D1171. https://doi.org/10.1093/nar/gky983
  2. Shepelin D, Hansen ASL, Lennen R, Luo H, Herrgard MJ. 2018. Selecting the best: evolutionary engineering of chemical production in microbes. Genes (Basel) 9: 249. https://doi.org/10.3390/genes9050249
  3. Bennett AF, Hughes BS. 2009. Microbial experimental evolution. 297: R17-R25. https://doi.org/10.1152/ajpregu.90562.2008
  4. Kering KK, Zhang X, Nyaruaba R, Yu J, Wei H. 2020. Application of adaptive evolution to improve the stability of bacteriophages during storage. Viruses 12: E423. https://doi.org/10.3390/v12040423
  5. Bailey LA, Hatton D, Field R, Dickson AJ. 2012. Determination of Chinese hamster ovary cell line stability and recombinant antibody expression during long-term culture. Biotechnol. Bioeng. 109: 2093-2103. https://doi.org/10.1002/bit.24485
  6. Cakar ZP, Turanli-Yildiz B, Alkim C, Yilmaz U. 2012. Evolutionary engineering of Saccharomyces cerevisiae for improved industrially important properties. FEMS Yeast Res. 12: 171-182. https://doi.org/10.1111/j.1567-1364.2011.00775.x
  7. Stella RG, Wiechert J, Noack S, Frunzke J. 2019. Evolutionary engineering of Corynebacterium glutamicum. Biotechnol. J. 14: e1800444. https://doi.org/10.1002/biot.201800444
  8. Zhou S, Shanmugam KT, Ingram LO. 2003. Functional replacement of the Escherichia coli D-(-)-lactate dehydrogenase gene (ldhA) with the L-(+)-lactate dehydrogenase gene (ldhL) from Pediococcus acidilactici. Appl. Environ. Microbiol. 69: 2237-2244. https://doi.org/10.1128/AEM.69.4.2237-2244.2003
  9. Zhu K, Lu L, Wei L, Wei D, Imanaka T, Hua Q. 2011. Modification and evolution of Gluconobacter oxydans for enhanced growth and biotransformation capabilities at low glucose concentration. Mol. Biotechnol. 49: 56-64. https://doi.org/10.1007/s12033-011-9378-6
  10. Nielsen J. 2017. Systems biology of metabolism. Annu. Rev. Biochem. 86: 245-275. https://doi.org/10.1146/annurev-biochem-061516-044757
  11. Ibarra RU, Edwards JS, Palsson BO. 2002. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420: 186-189. https://doi.org/10.1038/nature01149
  12. Choe D, Lee JH, Yoo M, Hwang S, Sung BH, Cho S, et al. 2019. Adaptive laboratory evolution of a genome-reduced Escherichia coli. Nat. Commun. 10: 935. https://doi.org/10.1038/s41467-019-08888-6
  13. Si T, Lian J, Zhao H. 2017. Strain Development by Whole-Cell Directed Evolution, pp. 173-200. In Alcalde M (ed.), Directed Enzyme Evolution: Advances and Applications, Ed. Springer International Publishing, Cham
  14. Conrad TM, Lewis NE, Palsson BO. 2011. Microbial laboratory evolution in the era of genome-scale science. Mol. Syst. Biol. 7: 509-509. https://doi.org/10.1038/msb.2011.42
  15. Portnoy VA, Bezdan D, Zengler K. 2011. Adaptive laboratory evolution-harnessing the power of biology for metabolic engineering. Curr. Opin. Biotechnol. 22: 590-594. https://doi.org/10.1016/j.copbio.2011.03.007
  16. Winkler JD, Kao KC. 2014. Recent advances in the evolutionary engineering of industrial biocatalysts. Genomics 104: 406-411. https://doi.org/10.1016/j.ygeno.2014.09.006
  17. Winkler J, Reyes LH, Kao KC. 2013. Adaptive laboratory evolution for strain engineering. Methods Mol. Biol. 985: 211-222. https://doi.org/10.1007/978-1-62703-299-5_11
  18. Elena SF, Lenski RE. 2003. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4: 457-469. https://doi.org/10.1038/nrg1088
  19. Choi KR, Jang WD, Yang D, Cho JS, Park D, Lee SY. 2019. Systems metabolic engineering strategies: Integrating systems and synthetic biology with metabolic engineering. Trends Biotechnol. 37: 817-837. https://doi.org/10.1016/j.tibtech.2019.01.003
  20. Grabar TB, Zhou S, Shanmugam KT, Yomano LP, Ingram LO. 2006. Methylglyoxal bypass identified as source of chiral contamination in l(+) and d(-)-lactate fermentations by recombinant Escherichia coli. Biotechnol. Lett. 28: 1527-1535. https://doi.org/10.1007/s10529-006-9122-7
  21. Royce LA, Yoon JM, Chen Y, Rickenbach E, Shanks JV, Jarboe LR. 2015. Evolution for exogenous octanoic acid tolerance improves carboxylic acid production and membrane integrity. Metab. Eng. 29: 180-188. https://doi.org/10.1016/j.ymben.2015.03.014
  22. Wang Y, Tian T, Zhao J, Wang J, Yan T, Xu L, et al. 2012. Homofermentative production of D-lactic acid from sucrose by a metabolically engineered Escherichia coli. Biotechnol. Lett. 34: 2069-2075. https://doi.org/10.1007/s10529-012-1003-7
  23. Zhao J, Xu L, Wang Y, Zhao X, Wang J, Garza E, et al. 2013. Homofermentative production of optically pure L-lactic acid from xylose by genetically engineered Escherichia coli B. Microb. Cell Fact. 12: 57. https://doi.org/10.1186/1475-2859-12-57
  24. Zhou S, Yomano LP, Shanmugam KT, Ingram LO. 2005. Fermentation of 10% (w/v) sugar to D: (-)-lactate by engineered Escherichia coli B. Biotechnol. Lett. 27: 1891-1896. https://doi.org/10.1007/s10529-005-3899-7
  25. Kim HJ, Jeong H, Lee SJ. 2020. Short-term adaptation modulates anaerobic metabolic flux to succinate by activating ExuT, a novel D-glucose transporter in Escherichia coli. Front. Microbiol. 11: 27. https://doi.org/10.3389/fmicb.2020.00027
  26. Otero JM, Cimini D, Patil KR, Poulsen SG, Olsson L, Nielsen J. 2013. Industrial systems biology of Saccharomyces cerevisiae enables novel succinic acid cell factory. PLoS One 8: e54144. https://doi.org/10.1371/journal.pone.0054144
  27. Leavitt JM, Wagner JM, Tu CC, Tong A, Liu Y, Alper HS. 2017. Biosensor-enabled directed evolution to improve muconic acid production in Saccharomyces cerevisiae. Biotechnol. J. 12(10). doi: 10.1002/biot.201600687.
  28. Jiang LY, Chen SG, Zhang YY, Liu JZ. 2013. Metabolic evolution of Corynebacterium glutamicum for increased production of Lornithine. BMC Biotechnol. 13: 47. https://doi.org/10.1186/1472-6750-13-47
  29. Mahr R, Gatgens C, Gatgens J, Polen T, Kalinowski J, Frunzke J. 2015. Biosensor-driven adaptive laboratory evolution of L-valine production in Corynebacterium glutamicum. Metab. Eng. 32: 184-194. https://doi.org/10.1016/j.ymben.2015.09.017
  30. Basso TO, de Kok S, Dario M, do Espirito-Santo JC, Muller G, Schlolg PS, et al. 2011. Engineering topology and kinetics of sucrose metabolism in Saccharomyces cerevisiae for improved ethanol yield. Metab. Eng. 13: 694-703. https://doi.org/10.1016/j.ymben.2011.09.005
  31. Vilela Lde F, de Araujo VP, Paredes Rde S, Bon EP, Torres FA, Neves BC, et al. 2015. Enhanced xylose fermentation and ethanol production by engineered Saccharomyces cerevisiae strain. AMB Express. 5: 16. https://doi.org/10.1186/s13568-015-0102-y
  32. Pontrelli S, Fricke RCB, Sakurai SSM, Putri SP, Fitz-Gibbon S, Chung M, et al. 2018. Directed strain evolution restructures metabolism for 1-butanol production in minimal media. Metab. Eng. 49: 153-163. https://doi.org/10.1016/j.ymben.2018.08.004
  33. Smith KM, Liao JC. 2011. An evolutionary strategy for isobutanol production strain development in Escherichia coli. Metab. Eng. 13: 674-681. https://doi.org/10.1016/j.ymben.2011.08.004
  34. Yu S, Zhao Q, Miao X, Shi J. 2013. Enhancement of lipid production in low-starch mutants Chlamydomonas reinhardtii by adaptive laboratory evolution. Bioresour. Technol. 147: 499-507. https://doi.org/10.1016/j.biortech.2013.08.069
  35. Yoneda A, Henson WR, Goldner NK, Park KJ, Forsberg KJ, Kim SJ, et al. 2016. Comparative transcriptomics elucidates adaptive phenol tolerance and utilization in lipid-accumulating Rhodococcus opacus PD630. Nucleic Acids Res. 44: 2240-2254. https://doi.org/10.1093/nar/gkw055
  36. Fu W, Guethmundsson O, Paglia G, Herjolfsson G, Andresson OS, Palsson BO, et al. 2013. Enhancement of carotenoid biosynthesis in the green microalga Dunaliella salina with light-emitting diodes and adaptive laboratory evolution. Appl. Microbiol. Biotechnol. 97: 2395-2403. https://doi.org/10.1007/s00253-012-4502-5
  37. Chou HH, Keasling JD. 2013. Programming adaptive control to evolve increased metabolite production. Nat. Commun. 4: 2595. https://doi.org/10.1038/ncomms3595
  38. Reyes LH, Gomez JM, Kao KC. 2014. Improving carotenoids production in yeast via adaptive laboratory evolution. Metab. Eng. 21: 26-33. https://doi.org/10.1016/j.ymben.2013.11.002
  39. Charusanti P, Fong NL, Nagarajan H, Pereira AR, Li HJ, Abate EA, et al. 2012. Exploiting adaptive laboratory evolution of Streptomyces clavuligerus for antibiotic discovery and overproduction. PLoS One 7: e33727. https://doi.org/10.1371/journal.pone.0033727
  40. Rathore SS, Ramamurthy V, Allen S, Selva Ganesan S, Ramakrishnan J. 2016.Novel approach of adaptive laboratory evolution: Triggers defense molecules in Streptomyces sp. against targeted pathogen. RSC Adv. 6: 96250-96262. https://doi.org/10.1039/C6RA15952D
  41. Jantama K, Haupt MJ, Svoronos SA, Zhang X, Moore JC, Shanmugam KT, et al. 2008. Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. Biotechnol. Bioeng. 99: 1140-1153. https://doi.org/10.1002/bit.21694
  42. Luo H, Hansen ASL, Yang L, Schneider K, Kristensen M, Christensen U, et al. 2019. Coupling S-adenosylmethionine-dependent methylation to growth: design and uses. PLoS Biol. 17: e2007050. https://doi.org/10.1371/journal.pbio.2007050
  43. Choi JW, Yim SS, Jeong KJ. 2018. Development of a high-copy-number plasmid via adaptive laboratory evolution of Corynebacterium glutamicum. Appl. Microbiol. Biotechnol. 102: 873-883. https://doi.org/10.1007/s00253-017-8653-2
  44. Guimaraes PM, Francois J, Parrou JL, Teixeira JA, Domingues L. 2008. Adaptive evolution of a lactose-consuming Saccharomyces cerevisiae recombinant. Appl. Environ. Microbiol. 74: 1748-1756. https://doi.org/10.1128/AEM.00186-08
  45. Kuyper M, Toirkens MJ, Diderich JA, Winkler AA, van Dijken JP, Pronk JT. 2005. Evolutionary engineering of mixed-sugar utilization by a xylose-fermenting Saccharomyces cerevisiae strain. FEMS Yeast Res. 5: 925-934. https://doi.org/10.1016/j.femsyr.2005.04.004
  46. Lee SM, Jellison T, Alper HS. 2014. Systematic and evolutionary engineering of a xylose isomerase-based pathway in Saccharomyces cerevisiae for efficient conversion yields. Biotechnol. Biofuels. 7: 122.
  47. Tuyishime P, Wang Y, Fan L, Zhang Q, Li Q, Zheng P, et al. 2018. Engineering Corynebacterium glutamicum for methanoldependent growth and glutamate production. Metab. Eng. 49: 220-231. https://doi.org/10.1016/j.ymben.2018.07.011
  48. Lu L, Wei L, Zhu K, Wei D, Hua Q. 2012. Combining metabolic engineering and adaptive evolution to enhance the production of dihydroxyacetone from glycerol by Gluconobacter oxydans in a low-cost way. Bioresour. Technol. 117: 317-324. https://doi.org/10.1016/j.biortech.2012.03.013
  49. Fong SS, Joyce AR, Palsson BO. 2005. Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res. 15: 1365-1372. https://doi.org/10.1101/gr.3832305
  50. Hua Q, Joyce AR, Palsson BO, Fong SS. 2007. Metabolic characterization of Escherichia coli strains adapted to growth on lactate. Appl. Environ. Microbiol. 73: 4639-4647. https://doi.org/10.1128/AEM.00527-07
  51. Lee DH, Palsson BO. 2010. Adaptive evolution of Escherichia coli K-12 MG1655 during growth on a nonnative carbon source, L-1,2-propanediol. Appl. Environ. Microbiol. 76: 4158-4168. https://doi.org/10.1128/AEM.00373-10
  52. Sandberg TE, Salazar MJ, Weng LL, Palsson BO, Feist AM. 2019. The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology. Metab. Eng. 56: 1-16. https://doi.org/10.1016/j.ymben.2019.08.004
  53. Dragosits M, Mattanovich D. 2013. Adaptive laboratory evolution - principles and applications for biotechnology. Microb. Cell Fact. 12: 64. https://doi.org/10.1186/1475-2859-12-64
  54. Graf M, Haas T, Muller F, Buchmann A, Harm-Bekbenbetova J, Freund A, et al. 2019. Continuous adaptive evolution of a fastgrowing Corynebacterium glutamicum strain independent of protocatechuate. Front. Microbiol. 10: 1648. https://doi.org/10.3389/fmicb.2019.01648
  55. Hoskisson PA, Hobbs G. 2005. Continuou culture - making a comeback? Microbiology 151: 3153-3159. https://doi.org/10.1099/mic.0.27924-0
  56. Jin T, Chen Y, Jarboe LR. 2016. Chapter 10 - Evolutionary methods for improving the production of biorenewable fuels and chemicals, pp. 265-290. In Eckert CA, Trinh CT (eds.), Biotechnology for Biofuel Production and Optimization, Ed. Elsevier, Amsterdam
  57. Herbert D, Elsworth R, Telling RC. 1956. The continuous culture of bacteria; a theoretical and experimental study. J. Gen. Microbiol. 14: 601-622. https://doi.org/10.1099/00221287-14-3-601
  58. Rao VSH, Rao PRS. 2004. Global stability in chemostat models involving time delays and wall growth. Nonlinear Analysis: Real World Applications. 5: 141-158. https://doi.org/10.1016/S1468-1218(03)00022-1
  59. Radek A, Tenhaef N, Muller MF, Brusseler C, Wiechert W, Marienhagen J, et al. 2017. Miniaturized and automated adaptive laboratory evolution: evolving Corynebacterium glutamicum towards an improved d-xylose utilization. Bioresour. Technol. 245: 1377-1385. https://doi.org/10.1016/j.biortech.2017.05.055
  60. Wu G, Yan Q, Jones JA, Tang YJ, Fong SS, Koffas MAG. 2016. Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol. 34: 652-664. https://doi.org/10.1016/j.tibtech.2016.02.010
  61. Schwentner A, Feith A, Munch E, Busche T, Ruckert C, Kalinowski J, et al. 2018. Metabolic engineering to guide evolution - creating a novel mode for L-valine production with Corynebacterium glutamicum. Metab. Eng. 47: 31-41. https://doi.org/10.1016/j.ymben.2018.02.015
  62. Lee S-W, Oh M-K. 2015. A synthetic suicide riboswitch for the high-throughput screening of metabolite production in Saccharomyces cerevisiae. Metab. Eng. 28: 143-150. https://doi.org/10.1016/j.ymben.2015.01.004
  63. Palsson BO. 2015. Adaptive Laboratory Evolution, pp. 422-437. Systems Biology: Constraint-based Reconstruction and Analysis, Ed. Cambridge University Press, Cambridge
  64. Lee DH, Feist AM, Barrett CL, Palsson BO. 2011. Cumulative number of cell divisions as a meaningful timescale for adaptive laboratory evolution of Escherichia coli. PLoS One 6: e26172. https://doi.org/10.1371/journal.pone.0026172
  65. Pfeifer E, Gatgens C, Polen T, Frunzke J. 2017. Adaptive laboratory evolution of Corynebacterium glutamicum towards higher growth rates on glucose minimal medium. Sci. Rep. 7: 16780. https://doi.org/10.1038/s41598-017-17014-9
  66. Larson MH, Gilbert LA, Wang X, Lim WA, Weissman JS, Qi LSJNp. 2013. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 8: 2180-2196. https://doi.org/10.1038/nprot.2013.132
  67. Perez-Pinera P, Kocak DD, Vockley CM, Adler AF, Kabadi AM, Polstein LR, et al. 2013. RNA-guided gene activation by CRISPRCas9-based transcription factors. Nat. Methods 10: 973-976. https://doi.org/10.1038/nmeth.2600
  68. Anzalone AV, Randolph PB, Davis JR, Sousa AA, Koblan LW, Levy JM, et al. 2019. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576: 149-157. https://doi.org/10.1038/s41586-019-1711-4
  69. Bodi Z, Farkas Z, Nevozhay D, Kalapis D, Lazar V, Csorgo B, et al. 2017. Phenotypic heterogeneity promotes adaptive evolution. PLoS Biol. 15: e2000644-e2000644. https://doi.org/10.1371/journal.pbio.2000644
  70. Sen M, Yilmaz U, Baysal A, Akman S, Cakar ZP. 2011. In vivo evolutionary engineering of a boron-resistant bacterium: Bacillus boroniphilus. Antonie van Leeuwenhoek. 99: 825-835. https://doi.org/10.1007/s10482-011-9557-2
  71. Sonderegger M, Sauer U. 2003. Evolutionary engineering of Saccharomyces cerevisiae for anaerobic growth on xylose. Appl. Environ. Microbiol. 69: 1990-1998. https://doi.org/10.1128/AEM.69.4.1990-1998.2003
  72. Camps M, Naukkarinen J, Johnson BP, Loeb LA. 2003. Targeted gene evolution in Escherichia coli using a highly error-prone DNA polymerase I. 100: 9727-9732. https://doi.org/10.1073/pnas.1333928100
  73. Ravikumar A, Arzumanyan GA, Obadi MKA, Javanpour AA, Liu CC. 2018. Scalable, continuous evolution of genes at mutation rates above genomic error thresholds. Cell 175: 1946-1957.e1913. https://doi.org/10.1016/j.cell.2018.10.021
  74. Moore CL, Papa LJ, 3rd, Shoulders MD. 2018. A processive protein chimera introduces mutations across defined DNA regions in vivo. J. Am. Chem. Soc. 140: 11560-11564. https://doi.org/10.1021/jacs.8b04001
  75. Halperin SO, Tou CJ, Wong EB, Modavi C, Schaffer DV, Dueber JE. 2018. CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window. Nature 560: 248-252. https://doi.org/10.1038/s41586-018-0384-8
  76. Jakociunas T, Pedersen LE, Lis AV, Jensen MK, Keasling JD. 2018. CasPER, a method for directed evolution in genomic contexts using mutagenesis and CRISPR/Cas9. Metab. Eng. 48: 288-296. https://doi.org/10.1016/j.ymben.2018.07.001
  77. Garst AD, Bassalo MC, Pines G, Lynch SA, Halweg-Edwards AL, Liu R, et al. 2017. Genome-wide mapping of mutations at singlenucleotide resolution for protein, metabolic and genome engineering. Nat. Biotechnol. 35: 48-55. https://doi.org/10.1038/nbt.3718
  78. Zhang Y-X, Perry K, Vinci VA, Powell K, Stemmer WPC, del Cardayre SB. 2002. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature 415: 644-646. https://doi.org/10.1038/415644a
  79. Alper H, Stephanopoulos G. 2007. Global transcription machinery engineering: a new approach for improving cellular phenotype. Metab. Eng. 9: 258-267. https://doi.org/10.1016/j.ymben.2006.12.002
  80. Wang HH, Isaacs FJ, Carr PA, Sun ZZ, Xu G, Forest CR, et al. 2009. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460: 894-898. https://doi.org/10.1038/nature08187
  81. Sandberg TE, Pedersen M, LaCroix RA, Ebrahim A, Bonde M, Herrgard MJ, et al. 2014. Evolution of Escherichia coli to $42^{\circ}C$ and subsequent genetic engineering reveals adaptive mechanisms and novel mutations. Mol. Biol. Evol. 31: 2647-2662. https://doi.org/10.1093/molbev/msu209
  82. Luan G, Cai Z, Li Y, Ma Y. 2013. Genome replication engineering assisted continuous evolution (GREACE) to improve microbial tolerance for biofuels production. Biotechnol. Biofuels. 6: 137. https://doi.org/10.1186/1754-6834-6-137
  83. Wang X, Li Q, Sun C, Cai Z, Zheng X, Guo X, et al. 2019. GREACE-assisted adaptive laboratory evolution in endpoint fermentation broth enhances lysine production by Escherichia coli. Microb. Cell Fact. 18: 106. https://doi.org/10.1186/s12934-019-1153-6
  84. McBryde C, Gardner JM, de Barros Lopes M, Jiranek V. 2006. Generation of Novel Wine Yeast Strains by Adaptive Evolution. Am. J. Enol. Vitic. 57: 423.
  85. Perez-Torrado R, Querol A, Guillamon JM. 2015. Genetic improvement of non-GMO wine yeasts: strategies, advantages and safety. Trends Food Sci. Technol. 45: 1-11. https://doi.org/10.1016/j.tifs.2015.05.002
  86. Borner RA, Kandasamy V, Axelsen AM, Nielsen AT, Bosma EF. 2019. Genome editing of lactic acid bacteria: opportunities for food, feed, pharma and biotech. FEMS Microbiol. Lett. 366: fny291. https://doi.org/10.1093/femsle/fny291
  87. Lusk JL, Roosen J, Bieberstein A. 2014. Consumer acceptance of new food technologies: causes and roots of controversies. Annu. Rev. Resour. Econ. 6: 381-405. https://doi.org/10.1146/annurev-resource-100913-012735
  88. Hoier E, Janzen T, Rattray F, Sorensen K, Borsting MW, Brockmann E, et al. 2010. The production, application and action of lactic cheese starter cultures, pp. 166-192. Technology of Cheesemaking, Ed.
  89. Gonzalez R, Tronchoni J, Quiros M, Morales P. 2016. Genetic improvement and genetically modified microorganisms, pp. 71-96. Wine Safety, Consumer Preference, and Human Health, Ed. Springer
  90. Csutak O, Sarbu I. 2018. Chapter 6 - Genetically Modified Microorganisms: Harmful or Helpful?, pp. 143-175. In Holban AM, Grumezescu AM (eds.), Genetically Engineered Foods,
  91. Snow R. 1983. Genetic improvement of wine yeast, pp. 439-459. Yeast genetics,
  92. Walker ME, Gardner JM, Vystavelova A, McBryde C, de Barros Lopes M, Jiranek VJFyr. 2003. Application of the reuseable, KanMX selectable marker to industrial yeast: construction and evaluation of heterothallic wine strains of Saccharomyces cerevisiae, possessing minimal foreign DNA sequences. 4: 339-347. https://doi.org/10.1016/S1567-1356(03)00161-2
  93. Bakalinsky AT, Snow R. 1990. The chromosomal constitution of wine strains of Saccharomyces cerevisiae. Yeast 6: 367-382. https://doi.org/10.1002/yea.320060503
  94. Querol A, Fernandez-Espinar MT, del Olmo M, Barrio E. 2003. Adaptive evolution of wine yeast. Int. J. Food Microbiol. 86: 3-10. https://doi.org/10.1016/S0168-1605(03)00244-7
  95. Denby CM, Li RA, Vu VT, Costello Z, Lin W, Chan LJG, et al. 2018. Industrial brewing yeast engineered for the production of primary flavor determinants in hopped beer. Nat. Commun. 9: 965. https://doi.org/10.1038/s41467-018-03293-x
  96. Pardo E, Rico J, Gil JV, Orejas M. 2015. De novo production of six key grape aroma monoterpenes by a geraniol synthase-engineered Saccharomyces cerevisiae wine strain. Microb. Cell Fact. 14: 136. https://doi.org/10.1186/s12934-015-0306-5
  97. Petzold CJ, Chan LJ, Nhan M, Adams PD. 2015. Analytics for metabolic engineering. Front. Bioeng. Biotechnol. 3: 135. https://doi.org/10.3389/fbioe.2015.00135
  98. Bergman A, Siewers V. 2016. Metabolic engineering strategies to convert carbohydrates to aviation range hydrocarbons, pp. 151-190. Biofuels for Aviation, Ed.
  99. Hansen ASL, Lennen RM, Sonnenschein N, Herrgard MJ. 2017. Systems biology solutions for biochemical production challenges. Curr. Opin. Biotechnol. 45: 85-91. https://doi.org/10.1016/j.copbio.2016.11.018
  100. Thiele I, Palsson BO. 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5: 93-121. https://doi.org/10.1038/nprot.2009.203
  101. Edwards JS, Palsson BO. 1999. Systems properties of the Haemophilus influenzaeRd metabolic genotype. J. Biol. Chem. 274: 17410-17416. https://doi.org/10.1074/jbc.274.25.17410
  102. O'Brien EJ, Monk JM, Palsson BO. 2015. Using genome-scale models to predict biological capabilities. Cell 161: 971-987. https://doi.org/10.1016/j.cell.2015.05.019
  103. Sandberg TE, Lloyd CJ, Palsson BO, Feist AM. 2017. Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies. Appl. Environ. Microbiol. 83: e00410-00417.
  104. LaCroix RA, Palsson BO, Feist AM. 2017. A model for designing adaptive laboratory evolution experiments. Appl. Environ. Microbiol. 83: e03115-03116.
  105. LaCroix RA, Sandberg TE, O'Brien EJ, Utrilla J, Ebrahim A, Guzman GI, et al. 2015. Use of adaptive laboratory evolution to discover key mutations enabling rapid growth of Escherichia coli K-12 MG1655 on glucose minimal medium. Appl. Environ. Microbiol. 81: 17-30. https://doi.org/10.1128/AEM.02246-14
  106. Strucko T, Zirngibl K, Pereira F, Kafkia E, Mohamed ET, Rettel M, et al. 2018. Laboratory evolution reveals regulatory and metabolic trade-offs of glycerol utilization in Saccharomyces cerevisiae. Metab. Eng. 47: 73-82. https://doi.org/10.1016/j.ymben.2018.03.006
  107. Sandberg TE, Long CP, Gonzalez JE, Feist AM, Antoniewicz MR, Palsson BO. 2016. Evolution of Escherichia coli on [U-13C]glucose reveals a negligible isotopic influence on metabolism and physiology. PLoS One 11: e0151130. https://doi.org/10.1371/journal.pone.0151130
  108. Horinouchi T, Minamoto T, Suzuki S, Shimizu H, Furusawa C. 2014. Development of an automated culture system for laboratory evolution. J. Lab. Autom. 19: 478-482. https://doi.org/10.1177/2211068214521417
  109. Toprak E, Veres A, Michel JB, Chait R, Hartl DL, Kishony R. 2011. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat .Genet. 44: 101-105.
  110. Toprak E, Veres A, Yildiz S, Pedraza JM, Chait R, Paulsson J, et al. 2013. Building a morbidostat: An automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nat. Protoc. 8: 555-567. https://doi.org/10.1038/nprot.2013.021
  111. Takahashi CN, Miller AW, Ekness F, Dunham MJ, Klavins E. 2015. A low cost, customizable turbidostat for use in Synthetic circuit characterization. ACS Synth. Biol. 4: 32-38. https://doi.org/10.1021/sb500165g
  112. Heins ZJ, Mancuso CP, Kiriakov S, Wong BG, Bashor CJ, Khalil AS. 2019. Designing automated, high-throughput, continuous cell growth experiments using eVOLVER. J. Vis. Exp. 147: 10.3791/59652.
  113. Wong BG, Mancuso CP, Kiriakov S, Bashor CJ, Khalil AS. 2018. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat. Biotechnol. 36: 614-623. https://doi.org/10.1038/nbt.4151
  114. Curran KA, Leavitt JM, Karim AS, Alper HS. 2013. Metabolic engineering of muconic acid production in Saccharomyces cerevisiae. Metab. Eng. 15: 55-66. https://doi.org/10.1016/j.ymben.2012.10.003
  115. Kao KC, Sherlock G. 2008. Molecular characterization of clonal interference during adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nat. Genet. 40: 1499-1504. https://doi.org/10.1038/ng.280
  116. Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, et al. 2006. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2: 2006.0008.
  117. Domingues L, Teixeira JA, Lima N. 1999. Construction of a flocculent Saccharomyces cerevisiae fermenting lactose. Appl. Microbiol. Biotechnol. 51: 621-626. https://doi.org/10.1007/s002530051441
  118. Kuyper M, Hartog MMP, Toirkens MJ, Almering MJH, Winkler AA, van Dijken JP, et al. 2005. Metabolic engineering of a xyloseisomerase-expressing Saccharomyces cerevisiae strain for rapid anaerobic xylose fermentation. FEMS Yeast Res. 5: 399-409. https://doi.org/10.1016/j.femsyr.2004.09.010
  119. Herring CD, Raghunathan A, Honisch C, Patel T, Applebee MK, Joyce AR, et al. 2006. Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nat. Genet. 38: 1406-1412. https://doi.org/10.1038/ng1906
  120. Monk JM, Lloyd CJ, Brunk E, Mih N, Sastry A, King Z, et al. 2017. iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotechnol. 35: 904-908. https://doi.org/10.1038/nbt.3956
  121. Lu H, Li F, Sanchez BJ, Zhu Z, Li G, Domenzain I, et al. 2019. A consensus Saccharomyces cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat. Commun. 10: 3586. https://doi.org/10.1038/s41467-019-11581-3
  122. Zhang Y, Cai J, Shang X, Wang B, Liu S, Chai X, et al. 2017. A new genome-scale metabolic model of Corynebacterium glutamicum and its application. Biotechnol. Biofuels 10: 169. https://doi.org/10.1186/s13068-017-0856-3
  123. Kocabas P, Calik P, Calik G, Ozdamar TH. 2017. Analyses of extracellular protein production in Bacillus subtilis - II: genome-scale metabolic model reconstruction based on updated gene-enzyme-reaction data. Biochem. Eng. J. 127: 229-241. https://doi.org/10.1016/j.bej.2017.07.005
  124. Lu Y, Ye C, Che J, Xu X, Shao D, Jiang C, et al. 2019. Genomic sequencing, genome-scale metabolic network reconstruction, and in silico flux analysis of the grape endophytic fungus Alternaria sp. MG1. Microb. Cell Fact. 18: 13. https://doi.org/10.1186/s12934-019-1063-7
  125. Kumelj T, Sulheim S, Wentzel A, Almaas E. 2019. Predicting strain engineering strategies using iKS1317: A genome-scale metabolic model of Streptomyces coelicolor. Biotechnol. J. 14: e1800180. https://doi.org/10.1002/biot.201800180
  126. Zuniga C, Levering J, Antoniewicz MR, Guarnieri MT, Betenbaugh MJ, Zengler K. 2018. Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris. Plant Physiol. 176: 450-462. https://doi.org/10.1104/pp.17.00605
  127. Ozcan E, Selvi SS, Nikerel E, Teusink B, Toksoy Oner E, Cakir T. 2019. A genome-scale metabolic network of the aroma bacterium Leuconostoc mesenteroides subsp. cremoris. Appl. Microbiol. Biotechnol. 103: 3153-3165. https://doi.org/10.1007/s00253-019-09630-4
  128. Kristjansdottir T, Bosma EF, Branco Dos Santos F, Ozdemir E, Herrgard MJ, Franca L, et al. 2019. A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory. Microb. Cell Fact. 18: 186. https://doi.org/10.1186/s12934-019-1229-3
  129. Loira N, Mendoza S, Paz Cortes M, Rojas N, Travisany D, Genova AD, et al. 2017. Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production. BMC Syst. Biol. 11: 66. https://doi.org/10.1186/s12918-017-0441-1
  130. Mora Salguero DA, Fernandez-Nino M, Serrano-Bermudez LM, Paez Melo DO, Winck FV, Caldana C, et al. 2018. Development of a chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different $CO_2$ levels. PeerJ. 6: e5528. https://doi.org/10.7717/peerj.5528

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