From qualitative to quantitative: the state of the art and challenges for plant synthetic biology

Abstract

Backgrounds: As an increasing number of synthetic switches and circuits have been created for plant systems and of synthetic products produced in plant chassis, plant synthetic biology is taking a strong foothold in agriculture and medicine. The ever-exploding data has also promoted the expansion of toolkits in this field. Genetic parts libraries and quantitative characterization approaches have been developed. However, plant synthetic biology is still in its infancy. The considerations for selecting biological parts to design and construct genetic circuits with predictable functions remain desired.

Results: In this article, we review the current biotechnological progresses in field of plant synthetic biology. Assembly standardization and quantitative approaches of genetic parts and genetic circuits are discussed. We also highlight the main challenges in the iterative cycles of design-build-test-learn for introducing novel traits into plants.

Conclusion: Plant synthetic biology promises to provide important solutions to many issues in agricultural production, human health care, and environmental sustainability. However, tremendous challenges exist in this field. For example, the quantitative characterization of genetic parts is limited; the orthogonality and the transfer functions of circuits are unpredictable; and also, the mathematical modeling-assisted circuits design still needs to improve predictability and reliability. These challenges are expected to be resolved in the near future as interests in this field are intensifying.

Graphical abstract

Keywords

plant synthetic biology / quantitative characterization / genetic parts / genetic circuits

References

[1] Endy, D. (2005). Foundations for engineering biology. Nature, 438: 449–453

[2] Zhang, L., Chang, S. (2011). Synthetic biology: from the first synthetic cell to see its current situation and future development. Chin. Sci. Bull., 56: 229–237

[3] Fausther-Bovendo, H. (2021). Plant-made vaccines and therapeutics. Science, 373: 740–741

[4] Zhu, X., Liu, X., Liu, T., Wang, Y., Ahmed, N., Li, Z. (2021). Synthetic biology of plant natural products: from pathway elucidation to engineered biosynthesis in plant cells. Plant Commun., 2: 100229

[5] Jiao, Y., Han, Y., Yang, Q., Huang, Y., An, J., Yang, Y. (2021). Commercialization development trend of genetically modified maize and the enlightenment. Shengwu Jishu Tongbao (in Chinese), 37: 164–176

[6] Imamura, T., Isozumi, N., Higashimura, Y., Ohki, S. (2021). Production of ORF8 protein from SARS-CoV-2 using an inducible virus-mediated expression system in suspension-cultured tobacco BY-2 cells. Plant Cell Rep., 40: 433–436

[7] Diego-Martin, B., lez, B., Vazquez-Vilar, M., Selma, S., ndez, R., Gianoglio, S., ndez-Del-Carmen, A. (2020). Pilot production of SARS-CoV-2 related proteins in plants: a proof of concept for rapid repurposing of indoor farms into biomanufacturing facilities. Front. Plant Sci., 11: 612781

[8] Breitel, D., Brett, P., Alseekh, S., Fernie, A. R., Butelli, E. (2021). Metabolic engineering of tomato fruit enriched in L-DOPA. Metab. Eng., 65: 185–196

[9] Molina-Hidalgo, F. J., Vazquez-Vilar, M., Andrea, L., Demurtas, O. C., Fraser, P., Giuliano, G., Bock, R., ez, D. (2021). Engineering metabolism in nicotiana species: a promising future. Trends Biotechnol., 39: 901–913

[10] Akama, K., Kanetou, J., Shimosaki, S., Kawakami, K., Tsuchikura, S. (2009). Seed-specific expression of truncated OsGAD2 produces GABA-enriched rice grains that influence a decrease in blood pressure in spontaneously hypertensive rats. Transgenic Res., 18: 865–876

[11] Zhu, Q., Wang, B., Tan, J., Liu, T., Li, L. Liu, Y. (2019). Plant synthetic metabolic engineering for enhancing crop nutritional quality. Plant Commun., 1: 100017

[12] ller, K., Siegel, D., Rodriguez Jahnke, F., Gerrer, K., Wend, S., Decker, E. L., Reski, R., Weber, W. Zurbriggen, M. (2014). A red light-controlled synthetic gene expression switch for plant systems. Mol. Biosyst., 10: 1679–1688

[13] Chatelle, C., Ochoa-Fernandez, R., Engesser, R., Schneider, N., Beyer, H. M., Jones, A. R., Timmer, J., Zurbriggen, M. D. (2018). A green-light-responsive system for the control of transgene expression in mammalian and plant cells. ACS Synth. Biol., 7: 1349–1358

[14] Khakhar, A., Leydon, A. R., Lemmex, A. C., Klavins, E. Nemhauser, J. (2018). Synthetic hormone-responsive transcription factors can monitor and re-program plant development. eLife, 7: e34702

[15] Gomide, M. S., Sales, T. T., Barros, L. R. C., Limia, C. G., de Oliveira, M. A., Florentino, L. H., Barros, L. M. G., Robledo, M. L., Almeida, M. S. M. . (2020). Genetic switches designed for eukaryotic cells and controlled by serine integrases. Commun. Biol., 3: 255

[16] -Orts, J. M., Quijano-Rubio, A., Vazquez-Vilar, M., o-Bonillo, J., Moles-Casas, V., Selma, S., Gianoglio, S., Granell, A. (2020). A memory switch for plant synthetic biology based on the phage ϕC31 integration system. Nucleic Acids Res., 48: 3379–3394

[17] Lloyd, J. P. B., Ly, F., Gong, P., Pflueger, J., Swain, T., Pflueger, C., Fourie, E., Khan, M. A., Kidd, B. N. (2022). Synthetic memory circuits for stable cell reprogramming in plants. Nat. Biotechnol., 40: 1862–1872

[18] Brophy, J. A. N., Magallon, K. J., Duan, L., Zhong, V., Ramachandran, P., Kniazev, K. Dinneny, J. (2022). Synthetic genetic circuits as a means of reprogramming plant roots. Science, 377: 747–751

[19] Liu, J., Li, C. Q., Dong, Y., Yang, X. Wang, Y. (2018). Dosage imbalance of B- and C-class genes causes petaloid-stamen relating to F₁ hybrid variation. BMC Plant Biol., 18: 341

[20] Dickinson, A. J., Zhang, J., Luciano, M., Wachsman, G., Sandoval, E., Schnermann, M., Dinneny, J. R. Benfey, P. (2021). A plant lipocalin promotes retinal-mediated oscillatory lateral root initiation. Science, 373: 1532–1536

[21] He, S., Yang, L., Ye, S., Lin, Y., Li, X., Wang, Y., Chen, G., Liu, G., Zhao, M., Zhao, X. . (2022). MPOD: Applications of integrated multi-omics database for medicinal plants. Plant Biotechnol. J., 20: 797–799

[22] Dreos, R., Ambrosini, G., Groux, R., rier, R. (2017). The eukaryotic promoter database in its 30th year: focus on non-vertebrate organisms. Nucleic Acids Res., 45: D51–D55

[23] Grau, J. Franco-Zorrilla, J. (2022). TDTHub, a web server tool for the analysis of transcription factor binding sites in plants. Plant J., 111: 1203–1215

[24] Kusunoki, K. Yamamoto, Y. (2017). Plant promoter database (PPDB). Methods Mol. Biol., 1533: 299–314

[25] Lescot, M., hais, P., Thijs, G., Marchal, K., Moreau, Y., Van de Peer, Y., (2002). PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res., 30: 325–327

[26] Kolar, K., Knobloch, C., Stork, H., (2018). OptoBase: a web platform for molecular optogenetics. ACS Synth. Biol., 7: 1825–1828

[27] Matys, V., Kel-Margoulis, O. V., Fricke, E., Liebich, I., Land, S., Barre-Dirrie, A., Reuter, I., Chekmenev, D., Krull, M., Hornischer, K. . (2006). TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res., 34: D108–D110

[28] Moisseyev, G., Park, K., Cui, A., Freitas, D., Rajagopal, D., Konda, A. R., Martin-Olenski, M., Mcham, M., Liu, K., Du, Q. . (2020). RGPDB: database of root-associated genes and promoters in maize, soybean, and sorghum. Database (Oxford), 2020: baaa038

[29] de Medeiros Oliveira, M., Bonadio, I., Lie de Melo, A., Mendes Souza, G. Durham, A. (2021). TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes. Brief. Bioinform., 22: bbab198

[30] Shahmuradov, I. A., Gammerman, A. J., Hancock, J. M., Bramley, P. M. Solovyev, V. (2003). PlantProm: a database of plant promoter sequences. Nucleic Acids Res., 31: 114–117

[31] Solovyev, V. V., Shahmuradov, I. A. Salamov, A. (2010). Identification of promoter regions and regulatory sites. Methods Mol. Biol., 674: 57–83

[32] Shahmuradov, I. A. Solovyev, V. (2015). Nsite, NsiteH and NsiteM computer tools for studying transcription regulatory elements. Bioinformatics, 31: 3544–3545

[33] Yilmaz, A., Mejia-Guerra, M. K., Kurz, K., Liang, X., Welch, L. (2011). AGRIS: the Arabidopsis gene regulatory information server, an update. Nucleic Acids Res., 39: D1118–D1122

[34] Hehl, R., Norval, L., Romanov, A. (2016). Boosting AthaMap database content with data from protein binding microarrays. Plant Cell Physiol., 57: e4

[35] Shahmuradov, I. A., Solovyev, V. V. Gammerman, A. (2005). Plant promoter prediction with confidence estimation. Nucleic Acids Res., 33: 1069–1076

[36] Shahmuradov, I. A., Umarov, R. K. Solovyev, V. (2017). TSSPlant: a new tool for prediction of plant Pol II promoters. Nucleic Acids Res., 45: gkw1353

[37] Cai, Y. M., Kallam, K., Tidd, H., Gendarini, G., Salzman, A. Patron, N. (2020). Rational design of minimal synthetic promoters for plants. Nucleic Acids Res., 48: 11845–11856

[38] Duvick, J., Fu, A., Muppirala, U., Sabharwal, M., Wilkerson, M. D., Lawrence, C. J., Lushbough, C. (2008). PlantGDB: a resource for comparative plant genomics. Nucleic Acids Res., 36: D959–D965

[39] Liu, Y., Wang, Z., Wu, X., Zhu, J., Luo, H., Tian, D., Li, C., Luo, J., Zhao, W., Hao, H. . (2021). SorGSD: updating and expanding the sorghum genome science database with new contents and tools. Biotechnol. Biofuels, 14: 165

[40] Chen, F., Dong, W., Zhang, J., Guo, X., Chen, J., Wang, Z., Lin, Z., Tang, H. (2018). The sequenced angiosperm genomes and genome databases. Front. Plant Sci., 9: 418

[41] Yang, Y., Lee, J. H., Poindexter, M. R., Shao, Y., Liu, W., Lenaghan, S. C., Ahkami, A. H., Blumwald, E. Stewart, C. N. (2021). Rational design and testing of abiotic stress-inducible synthetic promoters from poplar cis-regulatory elements. Plant Biotechnol. J., 19: 1354–1369

[42] Pedro, D. L. F., Amorim, T. S., Varani, A., Guyot, R., Domingues, D. S. Paschoal, A. (2021). An atlas of plant transposable elements. F1000 Res., 10: 1194

[43] Clarke, L. J. Kitney, R. (2016). Synthetic biology in the UK—An outline of plans and progress. Synth. Syst. Biotechnol., 1: 243–257

[44] Chung, S. M., Frankman, E. L. (2005). A versatile vector system for multiple gene expression in plants. Trends Plant Sci., 10: 357–361

[45] Nakagawa, T., Kurose, T., Hino, T., Tanaka, K., Kawamukai, M., Niwa, Y., Toyooka, K., Matsuoka, K., Jinbo, T. (2007). Development of series of gateway binary vectors, pGWBs, for realizing efficient construction of fusion genes for plant transformation. J. Biosci. Bioeng., 104: 34–41

[46] Smolke, C. (2009). Building outside of the box: iGEM and the BioBricks foundation. Nat. Biotechnol., 27: 1099–1102

[47] KnightT.. (2003) Idempotent Vector Design for Standard Assembly of Biobricks. Cambridge: Mit Artificial Intelligence Laboratory; Mit Synthetic Biology Working Group

[48] Smedley, M. A. Harwood, W. (2015). Gateway®-compatible plant transformation vectors. Methods Mol. Biol., 1223: 3–16

[49] Karimi, M., Depicker, A. (2007). Recombinational cloning with plant gateway vectors. Plant Physiol., 145: 1144–1154

[50] Karimi, M., Bleys, A., Vanderhaeghen, R. (2007). Building blocks for plant gene assembly. Plant Physiol., 145: 1183–1191

[51] s-Bueno, M. D. M., Morao, A. K., Cayrel, A., Platre, M. P., Barberon, M., Caillieux, E., Colot, V., Jaillais, Y., Roudier, F. (2016). A versatile Multisite Gateway-compatible promoter and transgenic line collection for cell type-specific functional genomics in Arabidopsis. Plant J., 85: 320–333

[52] Engler, C., Kandzia, R. (2008). A one pot, one step, precision cloning method with high throughput capability. PLoS One, 3: e3647

[53] Patron, N. J., Orzaez, D., Marillonnet, S., Warzecha, H., Matthewman, C., Youles, M., Raitskin, O., Leveau, A., Rogers, C. . (2015). Standards for plant synthetic biology: a common syntax for exchange of DNA parts. New Phytol., 208: 13–19

[54] Engler, C., Youles, M., Gruetzner, R., Ehnert, T. M., Werner, S., Jones, J. D., Patron, N. J. (2014). A golden gate modular cloning toolbox for plants. ACS Synth. Biol., 3: 839–843

[55] Sarrion-PerdigonesA.,FalconiE.ZandalinasS.rezP.,ndez-del-CarmenA.,GranellA.. (2011) GoldenBraid: an iterative cloning system for standardized assembly of reusable genetic modules. PLoS One. 6, e21622

[56] Sarrion-Perdigones, A., Vazquez-Vilar, M., Castelijns, B., Forment, J., Ziarsolo, P., Blanca, J., Granell, A. (2013). GoldenBraid 2. 0: a comprehensive DNA assembly framework for plant synthetic biology. Plant Physiol., 162: 1618–1631

[57] Vazquez-Vilar, M., Quijano-Rubio, A., Fernandez-Del-Carmen, A., Sarrion-Perdigones, A., Ochoa-Fernandez, R., Ziarsolo, P., Blanca, J., Granell, A. (2017). GB3.0: a platform for plant bio-design that connects functional DNA elements with associated biological data. Nucleic Acids Res., 45: 2196–2209

[58] Vazquez-VilarM.,Garcia-CarpinteroV.,SelmaS.,. M., Sanchez-Vicente, J., Salazar-Sarasua, B., Ressa, A., de Paola, C., Ajenjo, M., Quintela, J. C., et al. (2021) The GB4.0 platform, an All-In-One tool for CRISPR/Cas-based multiplex genome engineering in plants. Front. Plant Sci., 12, 689937

[59] Vazquez-Vilar, M., Juarez, P., -Orts, J. M. (2022). Design of multiplexing CRISPR/Cas9 constructs for plant genome engineering using the GoldenBraid DNA assembly standard. Methods Mol. Biol., 2379: 27–44

[60] lez, B., Vazquez-Vilar, M., nchez-Vicente, J. (2022). Optimization of vectors and targeting strategies including GoldenBraid and genome editing tools: GoldenBraid assembly of multiplex CRISPR/Cas12a guide RNAs for gene editing in Nicotiana benthamiana. Methods Mol. Biol., 2480: 193–214

[61] Vemanna, R. S., Chandrashekar, B. K., Hanumantha Rao, H. M., Sathyanarayanagupta, S. K., Sarangi, K. S., Nataraja, K. N. (2013). A modified MultiSite gateway cloning strategy for consolidation of genes in plants. Mol. Biotechnol., 53: 129–138

[62] Shih, P. M., Vuu, K., Mansoori, N., Ayad, L., Louie, K. B., Bowen, B. P., Northen, T. R. (2016). A robust gene-stacking method utilizing yeast assembly for plant synthetic biology. Nat. Commun., 7: 13215

[63] Zhu, Q., Zeng, D., Yu, S., Cui, C., Li, J., Li, H., Chen, J., Zhang, R., Zhao, X., Chen, L. . (2018). From golden rice to aSTARice: Bioengineering astaxanthin biosynthesis in rice endosperm. Mol. Plant, 11: 1440–1448

[64] Zhu, Q., Yu, S., Zeng, D., Liu, H., Wang, H., Yang, Z., Xie, X., Shen, R., Tan, J., Li, H. . (2017). Development of “purple endosperm rice” by engineering anthocyanin biosynthesis in the endosperm with a high-efficiency transgene stacking system. Mol. Plant, 10: 918–929

[65] Lin, L., Liu, Y. G., Xu, X. (2003). Efficient linking and transfer of multiple genes by a multigene assembly and transformation vector system. Proc. Natl. Acad. Sci. USA, 100: 5962–5967

[66] Zhao, Y., Han, J., Tan, J., Yang, Y., Li, S., Gou, Y., Luo, Y., Li, T., Xiao, W., Xue, Y. . (2022). Efficient assembly of long DNA fragments and multiple genes with improved nickase-based cloning and Cre/loxP recombination. Plant Biotechnol. J., 20: 1983–1995

[67] Altpeter, F., Springer, N. M., Bartley, L. E., Blechl, A. E., Brutnell, T. P., Citovsky, V., Conrad, L. J., Gelvin, S. B., Jackson, D. P., Kausch, A. P. . (2016). Advancing crop transformation in the era of genome editing. Plant Cell, 28: 1510–1520

[68] Schaumberg, K. A., Antunes, M. S., Kassaw, T. K., Xu, W., Zalewski, C. S., Medford, J. I. (2016). Quantitative characterization of genetic parts and circuits for plant synthetic biology. Nat. Methods, 13: 94–100

[69] Matsuo, N., Minami, M., Maeda, T. (2001). Dual luciferase assay for monitoring transient gene expression in higher plants. Plant Biotechnol. (Tsukuba), 18: 71–75

[70] Jores, T., Tonnies, J., Dorrity, M. W., Cuperus, J. T., Fields, S. (2020). Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves. Plant Cell, 32: 2120–2131

[71] Kim, Y. S., Johnson, G. D., Seo, J., Barrera, A., Cowart, T. N., Majoros, W. H., Ochoa, A., Allen, A. S. Reddy, T. (2021). Correcting signal biases and detecting regulatory elements in STARR-seq data. Genome Res., 31: 877–889

[72] Jores, T., Tonnies, J., Wrightsman, T., Buckler, E. S., Cuperus, J. T., Fields, S. (2021). Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters. Nat. Plants, 7: 842–855

[73] Sun, J., He, N., Niu, L., Huang, Y., Shen, W., Zhang, Y., Li, L. (2019). Global quantitative mapping of enhancers in rice by STARR-seq. Genom. Proteom. Bioinf., 17: 140–153

[74] TianC.,ZhangY.,LiJ.. (2022) Benchmarking intrinsic promoters and terminators for plant synthetic biology research. BioDesign Research., 2022

[75] AndreouA. I.,NirkkoJ.,Ochoa-VillarrealM.. (2021) Mobius assembly for plant systems highlights promoter-terminator interaction in gene regulation. bioRxiv doi: 10.1101/2021.03.31.437819

[76] Gunadi, A., Rushton, P. J., Mchale, L. K., Gutek, A. H. Finer, J. (2016). Characterization of 40 soybean (Glycine max) promoters, isolated from across 5 thematic gene groups. Plant Cell Tissue Organ Cult., 127: 1–16

[77] Kakei, Y., Masuda, H., Nishizawa, N. K., Hattori, H. Aung, M. (2021). Elucidation of novel cis-regulatory elements and promoter structures involved in iron excess response mechanisms in rice using a bioinformatics approach. Front. Plant Sci., 12: 660303

[78] Kaur, A., Pati, P. K., Pati, A. M. Nagpal, A. (2017). In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa. PLoS One, 12: e0184523

[79] Schmitz, R. J., Grotewold, E. (2022). Cis-regulatory sequences in plants: their importance, discovery, and future challenges. Plant Cell, 34: 718–741

[80] Basu, D. South, P. (2022). Design and analysis of native photorespiration gene motifs of promoter untranslated region combinations under short term abiotic stress conditions. Front. Plant Sci., 13: 828729

[81] To, J. P. C., Davis, I. W., Marengo, M. S., Shariff, A., Baublite, C., Decker, K., Gao, Z., Haragutchi, O., Jung, J. W. . (2021). Expression elements derived from plant sequences provide effective gene expression regulation and new opportunities for plant biotechnology traits. Front. Plant Sci., 12: 712179

[82] Davis, I. W., Benninger, C., Benfey, P. N. (2012). POWRS: position-sensitive motif discovery. PLoS One, 7: e40373

[83] McCarthy, D. M. Medford, J. (2020). Quantitative and predictive genetic parts for plant synthetic biology. Front. Plant Sci., 11: 512526

[84] Han, L., Silvestre, S., Sayanova, O., Haslam, R. P. Napier, J. (2022). Using field evaluation and systematic iteration to rationalise the accumulation of omega-3 long-chain polyunsaturated fatty acids in transgenic Camelina sativa. Plant Biotechnol. J., 20: 1833–1852

[85] Petrie, J. R., Zhou, X. R., Leonforte, A., McAllister, J., Shrestha, P., Kennedy, Y., Belide, S., Buzza, G., Gororo, N., Gao, W. . (2020). Development of a Brassica napus (Canola) crop containing fish oil-like levels of DHA in the seed oil. Front. Plant Sci., 11: 727

[86] Belide, S., Shrestha, P., Kennedy, Y., Leonforte, A., Devine, M. D., Petrie, J. R., Singh, S. P. Zhou, X. (2022). Engineering docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA) in Brassica juncea. Plant Biotechnol. J., 20: 19–21

[87] Yan-yan, L., Li-na, G., Cheng-zhen, L. Zhi-gang, M. Li, Y., Guo, L., Liang, C., Meng, Z., Tahira, S., Guo, S. (2022). Overexpression of Brassica napus cytosolic fructose-1,6-bisphosphatase and sedoheptulose-1,7-bisphosphatase genes significantly enhanced tobacco growth and biomass. J. Integr. Agric., 21: 49–59

[88] Fan, H., Liu, Y., Li, C. Y., Jiang, Y., Song, J. J., Yang, L., Zhao, Q., Hu, Y. H., Chen, X. Y. Xu, J. (2021). Engineering high coenzyme Q₁₀ tomato. Metab. Eng., 68: 86–93

[89] Forestier, E. C. F., Czechowski, T., Cording, A. C., Gilday, A. D., King, A. J., Brown, G. D. Graham, I. (2021). Developing a Nicotiana benthamiana transgenic platform for high-value diterpene production and candidate gene evaluation. Plant Biotechnol. J., 19: 1614–1623

[90] Davis, K., Gkotsi, D. S., Smith, D. R. M., Goss, R. J. M., Caputi, L. Connor, S. (2020). Nicotiana benthamiana as a transient expression host to produce auxin analogs. Front. Plant Sci., 11: 581675

[91] Allen, Q. M., Febres, V. J., Rathinasabapathi, B. Chaparro, J. (2022). Engineering a plant-derived astaxanthin synthetic pathway into Nicotiana benthamiana. Front. Plant Sci., 12: 831785

[92] Narayanan, N., Beyene, G., Chauhan, R. D., Gehan, J., Butts, P., Siritunga, D., Okwuonu, I., Woll, A., nez-Aguilar, D. M. . (2019). Biofortification of field-grown cassava by engineering expression of an iron transporter and ferritin. Nat. Biotechnol., 37: 144–151

[93] Liang, Q., Wang, K., Liu, X., Riaz, B., Jiang, L., Wan, X., Ye, X. (2019). Improved folate accumulation in genetically modified maize and wheat. J. Exp. Bot., 70: 1539–1551

[94] Liu, X., Ma, X., Wang, H., Li, S., Yang, W., Nugroho, R. D., Luo, L., Zhou, X., Tang, C., Fan, Y. . (2021). Metabolic engineering of astaxanthin-rich maize and its use in the production of biofortified eggs. Plant Biotechnol. J., 19: 1812–1823

[95] Nett, R. S., Lau, W. Sattely, E. (2020). Discovery and engineering of colchicine alkaloid biosynthesis. Nature, 584: 148–153

[96] Li, J., Mutanda, I., Wang, K., Yang, L., Wang, J. (2019). Chloroplastic metabolic engineering coupled with isoprenoid pool enhancement for committed taxanes biosynthesis in Nicotiana benthamiana. Nat. Commun., 10: 4850

[97] ndez, R., nez, E., Gianoglio, S., Quijano-Rubio, A., Rubert, A., Rambla, J. L., Vazquez-Vilar, M., Huet, E. . (2021). Production of volatile moth sex pheromones in transgenic Nicotiana benthamiana plants. BioDesign Research., 2021: 9891082

[98] Iacopino, S., Jurinovich, S., Cupellini, L., Piccinini, L., Cardarelli, F., Perata, P., Mennucci, B., Giuntoli, B. (2019). A synthetic oxygen sensor for plants based on animal hypoxia signaling. Plant Physiol., 179: 986–1000

[99] Nemhauser, J. L. Torii, K. (2016). Plant synthetic biology for molecular engineering of signalling and development. Nat. Plants, 2: 16010

[100] Swinnen, G., Goossens, A. (2016). Lessons from domestication: targeting cis-regulatory elements for crop improvement. Trends Plant Sci., 21: 506–515

[101] Andres, J., Blomeier, T. Zurbriggen, M. (2019). Synthetic switches and regulatory circuits in plants. Plant Physiol., 179: 862–884

[102] Zhou, Y., Ding, M., Gao, S., Yu-Strzelczyk, J., Krischke, M., Duan, X., Leide, J., Riederer, M., Mueller, M. J., Hedrich, R. . (2021). Optogenetic control of plant growth by a microbial rhodopsin. Nat. Plants, 7: 144–151

[103] Liu, L., Gallagher, J., Arevalo, E. D., Chen, R., Skopelitis, T., Wu, Q., Bartlett, M. (2021). Enhancing grain-yield-related traits by CRISPR-Cas9 promoter editing of maize CLE genes. Nat. Plants, 7: 287–294

[104] guez-Leal, D., Lemmon, Z. H., Man, J., Bartlett, M. E. Lippman, Z. (2017). Engineering quantitative trait variation for crop improvement by genome editing. Cell, 171: 470–480.e8

[105] Song, X., Meng, X., Guo, H., Cheng, Q., Jing, Y., Chen, M., Liu, G., Wang, B., Wang, Y., Li, J. . (2022). Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nat. Biotechnol., 40: 1403–1411

[106] Lowder, L. G., Zhou, J., Zhang, Y., Malzahn, A., Zhong, Z., Hsieh, T. F., Voytas, D. F., Zhang, Y. (2018). Robust transcriptional activation in plants using multiplexed CRISPR-Act2. 0 and mTALE-Act systems. Mol. Plant, 11: 245–256

[107] Lowder, L. G., Paul, J. W. (2017). Multiplexed transcriptional activation or repression in plants using CRISPR-dCas9-based systems. Methods Mol. Biol., 1629: 167–184

[108] Lowder, L. G., Zhang, D., Baltes, N. J., Paul, J. W. Tang, X., Zheng, X., Voytas, D. F., Hsieh, T. F., Zhang, Y. (2015). A CRISPR/Cas9 toolbox for multiplexed plant genome editing and transcriptional regulation. Plant Physiol., 169: 971–985

[109] Chavez, A., Scheiman, J., Vora, S., Pruitt, B. W., Tuttle, M., P R Iyer, E., Lin, S., Kiani, S., Guzman, C. D., Wiegand, D. J. . (2015). Highly efficient Cas9-mediated transcriptional programming. Nat. Methods, 12: 326–328

[110] Sajwan, S. (2019). Gene activation by dCas9-CBP and the SAM system differ in target preference. Sci. Rep., 9: 18104

[111] Zalatan, J. G., Lee, M. E., Almeida, R., Gilbert, L. A., Whitehead, E. H., La Russa, M., Tsai, J. C., Weissman, J. S., Dueber, J. E., Qi, L. S. . (2015). Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell, 160: 339–350

[112] Zhou, H., Liu, J., Zhou, C., Gao, N., Rao, Z., Li, H., Hu, X., Li, C., Yao, X., Shen, X. . (2018). In vivo simultaneous transcriptional activation of multiple genes in the brain using CRISPR-dCas9-activator transgenic mice. Nat. Neurosci., 21: 440–446

[113] Li, Z., Zhang, D., Xiong, X., Yan, B., Xie, W., Sheen, J. Li, J. (2017). A potent Cas9-derived gene activator for plant and mammalian cells. Nat. Plants, 3: 930–936

[114] Pan, C., Wu, X., Markel, K., Malzahn, A. A., Kundagrami, N., Sretenovic, S., Zhang, Y., Cheng, Y., Shih, P. M. (2021). CRISPR-Act3. 0 for highly efficient multiplexed gene activation in plants. Nat. Plants, 7: 942–953

[115] Selma, S., -Orts, J. M., Vazquez-Vilar, M., Diego-Martin, B., Ajenjo, M., Garcia-Carpintero, V., Granell, A. (2019). Strong gene activation in plants with genome-wide specificity using a new orthogonal CRISPR/Cas9-based programmable transcriptional activator. Plant Biotechnol. J., 17: 1703–1705

[116] Selma, S., Espinosa-Ruiz, A., Gianoglio, S., Lopez-Gresa, M. P., zquez-Vilar, M., Flors, V., Granell, A. (2022). Custom-made design of metabolite composition in N. benthamiana leaves using CRISPR activators. Plant Biotechnol. J., 20: 1578–1590

[117] Pan, C., Li, G., Malzahn, A. A., Cheng, Y., Leyson, B., Sretenovic, S., Gurel, F., Coleman, G. D. (2022). Boosting plant genome editing with a versatile CRISPR-Combo system. Nat. Plants, 8: 513–525

[118] Dey, N., Sarkar, S., Acharya, S. Maiti, I. (2015). Synthetic promoters in planta. Planta, 242: 1077–1094

[119] Aysha, J., Noman, M., Wang, F., Liu, W., Zhou, Y., Li, H. (2018). Synthetic promoters: designing the cis regulatory modules for controlled gene expression. Mol. Biotechnol., 60: 608–620

[120] PandiarajanR.. (2018) In vivo promoter engineering in plants: are we ready? Plant Sci., 277, 132–138

[121] Ouma, W. Z., Pogacar, K. (2018). Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. PLOS Comput. Biol., 14: e1006098

[122] Core, L. J., Martins, A. L., Danko, C. G., Waters, C. T., Siepel, A. Lis, J. (2014). Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet., 46: 1311–1320

[123] Ricci, W. A., Lu, Z., Ji, L., Marand, A. P., Ethridge, C. L., Murphy, N. G., Noshay, J. M., Galli, M., a-Guerra, M. K., . (2019). Widespread long-range cis-regulatory elements in the maize genome. Nat. Plants, 5: 1237–1249

[124] Fagny, M., Kuijjer, M. L., Stam, M., Joets, J., Turc, O., re, J., Pateyron, S., Venon, A. (2021). Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks. Front. Genet., 11: 606285

[125] Ding, W., Cheng, J., Guo, D., Mao, L., Li, J., Lu, L., Zhang, Y., Yang, J. (2018). Engineering the 5′UTR-mediated regulation of protein abundance in yeast using nucleotide sequence activity relationships. ACS Synth. Biol., 7: 2709–2714

[126] Mutalik, V. K., Guimaraes, J. C., Cambray, G., Lam, C., Christoffersen, M. J., Mai, Q. A., Tran, A. B., Paull, M., Keasling, J. D., Arkin, A. P. . (2013). Precise and reliable gene expression via standard transcription and translation initiation elements. Nat. Methods, 10: 354–360

[127] Chen, Y., Zhang, S., Young, E. M., Jones, T. S., Densmore, D. Voigt, C. (2020). Genetic circuit design automation for yeast. Nat. Microbiol., 5: 1349–1360

[128] Xia, P. F., Ling, H., Foo, J. L. Chang, M. (2019). Synthetic genetic circuits for programmable biological functionalities. Biotechnol. Adv., 37: 107393

[129] Van Brempt, M., Clauwaert, J., Mey, F., Stock, M., Maertens, J., Waegeman, W. (2020). Predictive design of sigma factor-specific promoters. Nat. Commun., 11: 5822

[130] Nielsen, A. A., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D. Voigt, C. (2016). Genetic circuit design automation. Science, 352: aac7341

[131] Jones, T. S., Oliveira, S. M. D., Myers, C. J., Voigt, C. A. (2022). Genetic circuit design automation with Cello 2. 0. Nat. Protoc., 17: 1097–1113

[132] Kwok, R. (2010). Five hard truths for synthetic biology. Nature, 463: 288–290

[133] Shin, J., Zhang, S., Der, B. S., Nielsen, A. A. Voigt, C. (2020). Programming Escherichia coli to function as a digital display. Mol. Syst. Biol., 16: e9401

[134] Crowther, M., Wipat, A. (2022). A network approach to genetic circuit designs. ACS Synth. Biol., 11: 3058–3066

[135] lez, A. (2019). Benefits of using genomic insulators flanking transgenes to increase expression and avoid positional effects. Sci. Rep., 9: 8474

[136] Puchta, H., Jiang, J., Wang, K. (2022). Updates on gene editing and its applications. Plant Physiol., 188: 1725–1730

[137] Neill, B. M., Mikkelson, K. L., Gutierrez, N. M., Cunningham, J. L., Wolff, K. L., Szyjka, S. J., Yohn, C. B., Redding, K. E. Mendez, M. (2012). An exogenous chloroplast genome for complex sequence manipulation in algae. Nucleic Acids Res., 40: 2782–2792

[138] Shao, Y., Lu, N., Wu, Z., Cai, C., Wang, S., Zhang, L. L., Zhou, F., Xiao, S., Liu, L., Zeng, X. . (2018). Creating a functional single-chromosome yeast. Nature, 560: 331–335

[139] Tomita, M. (2001). Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol., 19: 205–210

[140] Karr, J. R., Sanghvi, J. C., Macklin, D. N., Gutschow, M. V., Jacobs, J. M., Bolival, B. Assad-Garcia, N., Glass, J. I. Covert, M. (2012). A whole-cell computational model predicts phenotype from genotype. Cell, 150: 389–401

[141] Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I. . (2020). Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science, 369: eaav3751

[142] Maritan, M., Autin, L., Karr, J., Covert, M. W., Olson, A. J. Goodsell, D. (2022). Building structural models of a whole mycoplasma cell. J. Mol. Biol., 434: 167351

[143] Lu, H., Kerkhoven, E. J. (2022). Multiscale models quantifying yeast physiology: towards a whole-cell model. Trends Biotechnol., 40: 291–305

[144] Beard, D. A., Neal, M. L., Tabesh-Saleki, N., Thompson, C. T., Bassingthwaighte, J. B., Shimoyama, M. Carlson, B. (2012). Multiscale modeling and data integration in the virtual physiological rat project. Ann. Biomed. Eng., 40: 2365–2378

[145] Marshall-Colon, A., Long, S. P., Allen, D. K., Allen, G., Beard, D. A., Benes, B., von Caemmerer, S., Christensen, A. J., Cox, D. J., Hart, J. C. . (2017). Crops in silico: generating virtual crops using an integrative and multi-scale modeling platform. Front. Plant Sci., 8: 786

[146] Zhu, X. G., Lynch, J. P., LeBauer, D. S., Millar, A. J., Stitt, M. Long, S. (2016). Plants in silico: why, why now and what?—an integrative platform for plant systems biology research. Plant Cell Environ., 39: 1049–1057

[147] Zheng, H., Ho, P. Y., Jiang, M., Tang, B., Liu, W., Li, D., Yu, X., Kleckner, N. E., Amir, A. (2016). Interrogating the Escherichia coli cell cycle by cell dimension perturbations. Proc. Natl. Acad. Sci. USA, 113: 15000–15005

[148] Du, P., Zhao, H., Zhang, H., Wang, R., Huang, J., Tian, Y., Luo, X., Luo, X., Wang, M., Xiang, Y. . (2020). De novo design of an intercellular signaling toolbox for multi-channel cell-cell communication and biological computation. Nat. Commun., 11: 4226

[149] Milias-Argeitis, A., Rullan, M., Aoki, S. K., Buchmann, P. (2016). Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat. Commun., 7: 12546

[150] Olson, E. J., Hartsough, L. A., Landry, B. P., Shroff, R. Tabor, J. (2014). Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nat. Methods, 11: 449–455

[151] Carignano, A., Chen, D. H., Mallory, C., Wright, R. C., Seelig, G. (2022). Modular, robust, and extendible multicellular circuit design in yeast. eLife, 11: e74540

[152] Dorrity, M. W., Alexandre, C. M., Hamm, M. O., Vigil, A. L., Fields, S., Queitsch, C. Cuperus, J. (2021). The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. Nat. Commun., 12: 3334

[153] Farmer, A., Thibivilliers, S., Ryu, K. H., Schiefelbein, J. (2021). Single-nucleus RNA and ATAC sequencing reveals the impact of chromatin accessibility on gene expression in Arabidopsis roots at the single-cell level. Mol. Plant, 14: 372–383

[154] Okubo-Kurihara, E., Ali, A., Hiramoto, M., Kurihara, Y., Abouleila, Y., Abdelazem, E. M., Kawai, T., Makita, Y., Kawashima, M., Esaki, T. . (2022). Tracking metabolites at single-cell resolution reveals metabolic dynamics during plant mitosis. Plant Physiol., 189: 459–464

[155] Marand, A. P., Chen, Z., Gallavotti, A. Schmitz, R. (2021). A cis-regulatory atlas in maize at single-cell resolution. Cell, 184: 3041–3055.e21

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