Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Quantitative Biology

ISSN 2095-4689

ISSN 2095-4697(Online)

CN 10-1028/TM

Postal Subscription Code 80-971

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Quant. Biol.    2023, Vol. 11 Issue (3) : 214-230    https://doi.org/10.15302/J-QB-022-0326
REVIEW
From qualitative to quantitative: the state of the art and challenges for plant synthetic biology
Chenfei Tian1,2, Jianhua Li1(), Yong Wang1()
1. CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
2. University of Chinese Academy of Sciences, Beijing 100039, China
 Download: PDF(4621 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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.
Keywords plant synthetic biology      quantitative characterization      genetic parts      genetic circuits     
Corresponding Author(s): Jianhua Li,Yong Wang   
Just Accepted Date: 08 August 2023   Online First Date: 07 September 2023    Issue Date: 08 October 2023
 Cite this article:   
Chenfei Tian,Jianhua Li,Yong Wang. From qualitative to quantitative: the state of the art and challenges for plant synthetic biology[J]. Quant. Biol., 2023, 11(3): 214-230.
 URL:  
https://academic.hep.com.cn/qb/EN/10.15302/J-QB-022-0326
https://academic.hep.com.cn/qb/EN/Y2023/V11/I3/214
Databases and toolsDescriptionRefs.
Scientific databaseMultiple databases of multi-omics
MPODMulti-omics database for medicinal plants[21]
EPDThe eukaryotic promoter database[22]
TDTHubA web server tool for the analysis of transcription factor binding sited in plants[23]
PPDBA web-based plant promoter database[24]
PlantCAREA database of plant cis-acting regulatory elements, enhancers and repressors[25]
OptoBaseAn online platform for molecular optogenetics[26]
TRANSFACA database on eukaryotic transcription factors[27]
RGPDBA database of root-associated genes and promoters in maize, soybean, and sorghum[28]
TSSFinderA tool for prediction and annotation of different organisms[29]
PlantPromA database of plant promoter sequences[30]
TSSPPrediction of plant promoters[31]
NSITE-PLRecognition of plant regulatory motifs[32]
AGRISThe Arabidopsis gene regulatory information server[33]
AthaMapA genome-wide map of putative transcription factor binding sites in Arabidopsis thaliana[34]
Tab.1  Databases and tools for plant regulatory elements
Fig.1  Schematic diagram of DNA standardized assembly principles.
Fig.2  Schematic diagram of quantitative methods of various genetic parts.
Fig.3  Diagrammatic representation of rational design with computer tools.
ProductsPlant chassisThe number of genesUsed promotersUsed terminatorsRefs.
Omega-3 long chain polyunsaturated fatty acidsCamelina sativa seed;Stable transformation 9PUSP, PCNL, PSBP, PNP, PPvArc, PGLY, PCsVMVT35STOCS,TCatpA, TE9, TPvArc, TNOS, THSP, TPhaseolin, TGly[84]
Omega-3 long chain polyunsaturated fatty acidsBrassica napus seed; Brassica juncea seedStable transformation 9PFP1, PFAE1, PCNL1, PCNL2, P35STNOS, TLectin, TCNL1, TCNL2[85,86]
BiomassNicotiana tabacumStable transformation 3PNOS, P35STNOS[87]
CoQ10Solanum lycopersicum fruitStable transformation5PE8, P35STAtHSP, T35S[88]
Casbene-derived diterpenoidsNicotiana benthamiana leafTransient transformation; stable transformation6P35S, PStSTLS, PAtCab1, PAtuNos, PAtuMAS, PSlRbcS1, PSlRbcS2, PAtRbcS1B, PSlRbcS3A, PSIH4, PAtLHB1B1TAtuMAS, TAtug7, TAtuOCS, TAtuNos[89]
Auxin analogsN. benthamiana leafTransient transformation4P35STCaMV, TpolyA[90]
AstaxanthinN. benthamianaStable transformation2P35S; PFMVm 34STNOS, T35S[91]
Iron and zinc concentrations Manihot esculenta storage rootStable transformation2PA14, Ppatatin1T35S, TNOS[92]
Folate Z. mays and Triticum aestivum endospermStable transformation3P35S, PLeg1A, PGluCT35S, TNOS[93]
AstaxanthinZea mays seedStable transformation9P35S, P2BDEN (PR5SGPA/P2R5SGPA)T35S, TNOS[94]
N-formyldemecolcineN. benthamiana leafTransient transformation16P35STNOS[95]
Taxadiene-5α-olN. benthamiana leafTransient transformation4P35STNOS[96]
PheromoneN. benthamianaStable transformation4P35ST35S[97]
ViolaceinN. benthamiana leafTransient transformation5PAtHsp18, PMAS, PAct2, PRbcS, PBch1TAtHsp, TAtRbcS, TAtAct2, TAtUbq3, TNOS[62]
AstaxanthinRice endospermStable transformation4PGlub1, PGlub4, PGlb1, PGluCTmas, Tags, T35S[63]
AnthocyaninsRice endospermStable transformation8P10KDa, P16KDa, Pnpr33, PGlub5, PGlb1, PGlub4, PGlub1, PGluCTrbc, Tmas, TNOS, Tocs, Tags, T35S[64]
Tab.2  Engineering of multiple genes and the corresponding regulatory elements used in recent years
Fig.4  An example of an automated design-construct module.
1 D. Endy, (2005). Foundations for engineering biology. Nature, 438: 449–453
https://doi.org/10.1038/nature04342
2 L., Zhang, S. Chang, (2011). Synthetic biology: from the first synthetic cell to see its current situation and future development. Chin. Sci. Bull., 56: 229–237
https://doi.org/10.1007/s11434-010-4304-z
3 H. Fausther-Bovendo, (2021). Plant-made vaccines and therapeutics. Science, 373: 740–741
https://doi.org/10.1126/science.abf5375
4 X., Zhu, X., Liu, T., Liu, Y., Wang, N., Ahmed, Z. Li, (2021). Synthetic biology of plant natural products: from pathway elucidation to engineered biosynthesis in plant cells. Plant Commun., 2: 100229
https://doi.org/10.1016/j.xplc.2021.100229
5 Y., Jiao, Y., Han, Q., Yang, Y., Huang, J., An, Y. Yang, (2021). Commercialization development trend of genetically modified maize and the enlightenment. Shengwu Jishu Tongbao (in Chinese), 37: 164–176
6 T., Imamura, N., Isozumi, Y., Higashimura, S. Ohki, (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
https://doi.org/10.1007/s00299-020-02654-5
7 B., Diego-Martin, B., lez, M., Vazquez-Vilar, S., Selma, R., ndez, S., Gianoglio, A. ndez-Del-Carmen, (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
https://doi.org/10.3389/fpls.2020.612781
8 D., Breitel, P., Brett, S., Alseekh, A. R., Fernie, E. Butelli, (2021). Metabolic engineering of tomato fruit enriched in L-DOPA. Metab. Eng., 65: 185–196
https://doi.org/10.1016/j.ymben.2020.11.011
9 F. J., Molina-Hidalgo, M., Vazquez-Vilar, L., Andrea, O. C., Demurtas, P., Fraser, G., Giuliano, R., Bock, D. ez, (2021). Engineering metabolism in nicotiana species: a promising future. Trends Biotechnol., 39: 901–913
https://doi.org/10.1016/j.tibtech.2020.11.012
10 K., Akama, J., Kanetou, S., Shimosaki, K., Kawakami, S. Tsuchikura, (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
https://doi.org/10.1007/s11248-009-9272-1
11 Q., Zhu, B., Wang, J., Tan, T., Liu, L. Li, Y. Liu, (2019). Plant synthetic metabolic engineering for enhancing crop nutritional quality. Plant Commun., 1: 100017
https://doi.org/10.1016/j.xplc.2019.100017
12 K., ller, D., Siegel, F., Rodriguez Jahnke, K., Gerrer, S., Wend, E. L., Decker, R., Reski, W. Weber, M. Zurbriggen, (2014). A red light-controlled synthetic gene expression switch for plant systems. Mol. Biosyst., 10: 1679–1688
https://doi.org/10.1039/C3MB70579J
13 C., Chatelle, R., Ochoa-Fernandez, R., Engesser, N., Schneider, H. M., Beyer, A. R., Jones, J., Timmer, M. D. Zurbriggen, (2018). A green-light-responsive system for the control of transgene expression in mammalian and plant cells. ACS Synth. Biol., 7: 1349–1358
https://doi.org/10.1021/acssynbio.7b00450
14 A., Khakhar, A. R., Leydon, A. C., Lemmex, E. Klavins, J. Nemhauser, (2018). Synthetic hormone-responsive transcription factors can monitor and re-program plant development. eLife, 7: e34702
https://doi.org/10.7554/eLife.34702
15 M. S., Gomide, T. T., Sales, L. R. C., Barros, C. G., Limia, M. A., de Oliveira, L. H., Florentino, L. M. G., Barros, M. L., Robledo, M. S. M. Almeida, et al.. (2020). Genetic switches designed for eukaryotic cells and controlled by serine integrases. Commun. Biol., 3: 255
https://doi.org/10.1038/s42003-020-0971-8
16 J. M., -Orts, A., Quijano-Rubio, M., Vazquez-Vilar, J., o-Bonillo, V., Moles-Casas, S., Selma, S., Gianoglio, A. Granell, (2020). A memory switch for plant synthetic biology based on the phage ϕC31 integration system. Nucleic Acids Res., 48: 3379–3394
https://doi.org/10.1093/nar/gkaa104
17 J. P. B., Lloyd, F., Ly, P., Gong, J., Pflueger, T., Swain, C., Pflueger, E., Fourie, M. A., Khan, B. N. Kidd, (2022). Synthetic memory circuits for stable cell reprogramming in plants. Nat. Biotechnol., 40: 1862–1872
https://doi.org/10.1038/s41587-022-01383-2
18 J. A. N., Brophy, K. J., Magallon, L., Duan, V., Zhong, P., Ramachandran, K. Kniazev, J. Dinneny, (2022). Synthetic genetic circuits as a means of reprogramming plant roots. Science, 377: 747–751
https://doi.org/10.1126/science.abo4326
19 J., Liu, C. Q., Li, Y., Dong, X. Yang, Y. Wang, (2018). Dosage imbalance of B- and C-class genes causes petaloid-stamen relating to F1 hybrid variation. BMC Plant Biol., 18: 341
https://doi.org/10.1186/s12870-018-1562-4
20 A. J., Dickinson, J., Zhang, M., Luciano, G., Wachsman, E., Sandoval, M., Schnermann, J. R. Dinneny, P. Benfey, (2021). A plant lipocalin promotes retinal-mediated oscillatory lateral root initiation. Science, 373: 1532–1536
https://doi.org/10.1126/science.abf7461
21 S., He, L., Yang, S., Ye, Y., Lin, X., Li, Y., Wang, G., Chen, G., Liu, M., Zhao, X. Zhao, et al.. (2022). MPOD: Applications of integrated multi-omics database for medicinal plants. Plant Biotechnol. J., 20: 797–799
https://doi.org/10.1111/pbi.13769
22 R., Dreos, G., Ambrosini, R., Groux, R. rier, (2017). The eukaryotic promoter database in its 30th year: focus on non-vertebrate organisms. Nucleic Acids Res., 45: D51–D55
https://doi.org/10.1093/nar/gkw1069
23 J. Grau, J. Franco-Zorrilla, (2022). TDTHub, a web server tool for the analysis of transcription factor binding sites in plants. Plant J., 111: 1203–1215
https://doi.org/10.1111/tpj.15873
24 K. Kusunoki, Y. Yamamoto, (2017). Plant promoter database (PPDB). Methods Mol. Biol., 1533: 299–314
https://doi.org/10.1007/978-1-4939-6658-5_18
25 M., Lescot, P., hais, G., Thijs, K., Marchal, Y., Moreau, Y., Van de Peer, (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
https://doi.org/10.1093/nar/30.1.325
26 K., Kolar, C., Knobloch, H., Stork, (2018). OptoBase: a web platform for molecular optogenetics. ACS Synth. Biol., 7: 1825–1828
https://doi.org/10.1021/acssynbio.8b00120
27 V., Matys, O. V., Kel-Margoulis, E., Fricke, I., Liebich, S., Land, A., Barre-Dirrie, I., Reuter, D., Chekmenev, M., Krull, K. Hornischer, et al.. (2006). TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res., 34: D108–D110
https://doi.org/10.1093/nar/gkj143
28 G., Moisseyev, K., Park, A., Cui, D., Freitas, D., Rajagopal, A. R., Konda, M., Martin-Olenski, M., Mcham, K., Liu, Q. Du, et al.. (2020). RGPDB: database of root-associated genes and promoters in maize, soybean, and sorghum. Database (Oxford), 2020: baaa038
https://doi.org/10.1093/database/baaa038
29 M., de Medeiros Oliveira, I., Bonadio, A., Lie de Melo, G. Mendes Souza, A. Durham, (2021). TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes. Brief. Bioinform., 22: bbab198
https://doi.org/10.1093/bib/bbab198
30 I. A., Shahmuradov, A. J., Gammerman, J. M., Hancock, P. M. Bramley, V. Solovyev, (2003). PlantProm: a database of plant promoter sequences. Nucleic Acids Res., 31: 114–117
https://doi.org/10.1093/nar/gkg041
31 V. V., Solovyev, I. A. Shahmuradov, A. Salamov, (2010). Identification of promoter regions and regulatory sites. Methods Mol. Biol., 674: 57–83
https://doi.org/10.1007/978-1-60761-854-6_5
32 I. A. Shahmuradov, V. Solovyev, (2015). Nsite, NsiteH and NsiteM computer tools for studying transcription regulatory elements. Bioinformatics, 31: 3544–3545
https://doi.org/10.1093/bioinformatics/btv404
33 A., Yilmaz, M. K., Mejia-Guerra, K., Kurz, X., Liang, L. Welch, (2011). AGRIS: the Arabidopsis gene regulatory information server, an update. Nucleic Acids Res., 39: D1118–D1122
https://doi.org/10.1093/nar/gkq1120
34 R., Hehl, L., Norval, A. Romanov, (2016). Boosting AthaMap database content with data from protein binding microarrays. Plant Cell Physiol., 57: e4
https://doi.org/10.1093/pcp/pcv156
35 I. A., Shahmuradov, V. V. Solovyev, A. Gammerman, (2005). Plant promoter prediction with confidence estimation. Nucleic Acids Res., 33: 1069–1076
https://doi.org/10.1093/nar/gki247
36 I. A., Shahmuradov, R. K. Umarov, V. Solovyev, (2017). TSSPlant: a new tool for prediction of plant Pol II promoters. Nucleic Acids Res., 45: gkw1353
https://doi.org/10.1093/nar/gkw1353
37 Y. M., Cai, K., Kallam, H., Tidd, G., Gendarini, A. Salzman, N. Patron, (2020). Rational design of minimal synthetic promoters for plants. Nucleic Acids Res., 48: 11845–11856
https://doi.org/10.1093/nar/gkaa682
38 J., Duvick, A., Fu, U., Muppirala, M., Sabharwal, M. D., Wilkerson, C. J., Lawrence, C. Lushbough, (2008). PlantGDB: a resource for comparative plant genomics. Nucleic Acids Res., 36: D959–D965
https://doi.org/10.1093/nar/gkm1041
39 Y., Liu, Z., Wang, X., Wu, J., Zhu, H., Luo, D., Tian, C., Li, J., Luo, W., Zhao, H. Hao, et al.. (2021). SorGSD: updating and expanding the sorghum genome science database with new contents and tools. Biotechnol. Biofuels, 14: 165
https://doi.org/10.1186/s13068-021-02016-7
40 F., Chen, W., Dong, J., Zhang, X., Guo, J., Chen, Z., Wang, Z., Lin, H. Tang, (2018). The sequenced angiosperm genomes and genome databases. Front. Plant Sci., 9: 418
https://doi.org/10.3389/fpls.2018.00418
41 Y., Yang, J. H., Lee, M. R., Poindexter, Y., Shao, W., Liu, S. C., Lenaghan, A. H., Ahkami, E. Blumwald, C. N. Stewart, (2021). Rational design and testing of abiotic stress-inducible synthetic promoters from poplar cis-regulatory elements. Plant Biotechnol. J., 19: 1354–1369
https://doi.org/10.1111/pbi.13550
42 D. L. F., Pedro, T. S., Amorim, A., Varani, R., Guyot, D. S. Domingues, A. Paschoal, (2021). An atlas of plant transposable elements. F1000 Res., 10: 1194
https://doi.org/10.12688/f1000research.74524.1
43 L. J. Clarke, R. Kitney, (2016). Synthetic biology in the UK—An outline of plans and progress. Synth. Syst. Biotechnol., 1: 243–257
https://doi.org/10.1016/j.synbio.2016.09.003
44 S. M., Chung, E. L. Frankman, (2005). A versatile vector system for multiple gene expression in plants. Trends Plant Sci., 10: 357–361
https://doi.org/10.1016/j.tplants.2005.06.001
45 T., Nakagawa, T., Kurose, T., Hino, K., Tanaka, M., Kawamukai, Y., Niwa, K., Toyooka, K., Matsuoka, T. Jinbo, (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
https://doi.org/10.1263/jbb.104.34
46 C. Smolke, (2009). Building outside of the box: iGEM and the BioBricks foundation. Nat. Biotechnol., 27: 1099–1102
https://doi.org/10.1038/nbt1209-1099
47 T. Knight. (2003) Idempotent Vector Design for Standard Assembly of Biobricks. Cambridge: Mit Artificial Intelligence Laboratory; Mit Synthetic Biology Working Group
48 M. A. Smedley, W. Harwood, (2015). Gateway®-compatible plant transformation vectors. Methods Mol. Biol., 1223: 3–16
https://doi.org/10.1007/978-1-4939-1695-5_1
49 M., Karimi, A. Depicker, (2007). Recombinational cloning with plant gateway vectors. Plant Physiol., 145: 1144–1154
https://doi.org/10.1104/pp.107.106989
50 M., Karimi, A., Bleys, R. Vanderhaeghen, (2007). Building blocks for plant gene assembly. Plant Physiol., 145: 1183–1191
https://doi.org/10.1104/pp.107.110411
51 M. D. M., s-Bueno, A. K., Morao, A., Cayrel, M. P., Platre, M., Barberon, E., Caillieux, V., Colot, Y., Jaillais, F. Roudier, (2016). A versatile Multisite Gateway-compatible promoter and transgenic line collection for cell type-specific functional genomics in Arabidopsis. Plant J., 85: 320–333
https://doi.org/10.1111/tpj.13099
52 C., Engler, R. Kandzia, (2008). A one pot, one step, precision cloning method with high throughput capability. PLoS One, 3: e3647
https://doi.org/10.1371/journal.pone.0003647
53 N. J., Patron, D., Orzaez, S., Marillonnet, H., Warzecha, C., Matthewman, M., Youles, O., Raitskin, A., Leveau, C. Rogers, et al.. (2015). Standards for plant synthetic biology: a common syntax for exchange of DNA parts. New Phytol., 208: 13–19
https://doi.org/10.1111/nph.13532
54 C., Engler, M., Youles, R., Gruetzner, T. M., Ehnert, S., Werner, J. D., Jones, N. J. Patron, (2014). A golden gate modular cloning toolbox for plants. ACS Synth. Biol., 3: 839–843
https://doi.org/10.1021/sb4001504
55 A., Sarrion-PerdigonesE. FalconiS. ZandalinasP., rezA., ndez-del-CarmenA. Granell. (2011) GoldenBraid: an iterative cloning system for standardized assembly of reusable genetic modules. PLoS One. 6, e21622
56 A., Sarrion-Perdigones, M., Vazquez-Vilar, B., Castelijns, J., Forment, P., Ziarsolo, J., Blanca, A. Granell, (2013). GoldenBraid 2. 0: a comprehensive DNA assembly framework for plant synthetic biology. Plant Physiol., 162: 1618–1631
https://doi.org/10.1104/pp.113.217661
57 M., Vazquez-Vilar, A., Quijano-Rubio, A., Fernandez-Del-Carmen, A., Sarrion-Perdigones, R., Ochoa-Fernandez, P., Ziarsolo, J., Blanca, A. Granell, (2017). GB3.0: a platform for plant bio-design that connects functional DNA elements with associated biological data. Nucleic Acids Res., 45: 2196–2209
https://doi.org/10.1093/nar/gkw1326.PMID:28053117
58 M., Vazquez-VilarV., Garcia-CarpinteroS., Selma. 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 M., Vazquez-Vilar, P., Juarez, J. M. -Orts, (2022). Design of multiplexing CRISPR/Cas9 constructs for plant genome engineering using the GoldenBraid DNA assembly standard. Methods Mol. Biol., 2379: 27–44
https://doi.org/10.1007/978-1-0716-1791-5_2
60 B., lez, M., Vazquez-Vilar, J. nchez-Vicente, (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
https://doi.org/10.1007/978-1-0716-2241-4_12
61 R. S., Vemanna, B. K., Chandrashekar, H. M., Hanumantha Rao, S. K., Sathyanarayanagupta, K. S., Sarangi, K. N. Nataraja, (2013). A modified MultiSite gateway cloning strategy for consolidation of genes in plants. Mol. Biotechnol., 53: 129–138
https://doi.org/10.1007/s12033-012-9499-6
62 P. M., Shih, K., Vuu, N., Mansoori, L., Ayad, K. B., Louie, B. P., Bowen, T. R. Northen, (2016). A robust gene-stacking method utilizing yeast assembly for plant synthetic biology. Nat. Commun., 7: 13215
https://doi.org/10.1038/ncomms13215
63 Q., Zhu, D., Zeng, S., Yu, C., Cui, J., Li, H., Li, J., Chen, R., Zhang, X., Zhao, L. Chen, et al.. (2018). From golden rice to aSTARice: Bioengineering astaxanthin biosynthesis in rice endosperm. Mol. Plant, 11: 1440–1448
https://doi.org/10.1016/j.molp.2018.09.007
64 Q., Zhu, S., Yu, D., Zeng, H., Liu, H., Wang, Z., Yang, X., Xie, R., Shen, J., Tan, H. Li, et al.. (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
https://doi.org/10.1016/j.molp.2017.05.008
65 L., Lin, Y. G., Liu, X. Xu, (2003). Efficient linking and transfer of multiple genes by a multigene assembly and transformation vector system. Proc. Natl. Acad. Sci. USA, 100: 5962–5967
https://doi.org/10.1073/pnas.0931425100
66 Y., Zhao, J., Han, J., Tan, Y., Yang, S., Li, Y., Gou, Y., Luo, T., Li, W., Xiao, Y. Xue, et al.. (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
https://doi.org/10.1111/pbi.13882
67 F., Altpeter, N. M., Springer, L. E., Bartley, A. E., Blechl, T. P., Brutnell, V., Citovsky, L. J., Conrad, S. B., Gelvin, D. P., Jackson, A. P. Kausch, et al.. (2016). Advancing crop transformation in the era of genome editing. Plant Cell, 28: 1510–1520
https://doi.org/10.1105/tpc.16.00196
68 K. A., Schaumberg, M. S., Antunes, T. K., Kassaw, W., Xu, C. S., Zalewski, J. I. Medford, (2016). Quantitative characterization of genetic parts and circuits for plant synthetic biology. Nat. Methods, 13: 94–100
https://doi.org/10.1038/nmeth.3659
69 N., Matsuo, M., Minami, T. Maeda, (2001). Dual luciferase assay for monitoring transient gene expression in higher plants. Plant Biotechnol. (Tsukuba), 18: 71–75
https://doi.org/10.5511/plantbiotechnology.18.71
70 T., Jores, J., Tonnies, M. W., Dorrity, J. T., Cuperus, S. Fields, (2020). Identification of plant enhancers and their constituent elements by STARR-seq in tobacco leaves. Plant Cell, 32: 2120–2131
https://doi.org/10.1105/tpc.20.00155
71 Y. S., Kim, G. D., Johnson, J., Seo, A., Barrera, T. N., Cowart, W. H., Majoros, A., Ochoa, A. S. Allen, T. Reddy, (2021). Correcting signal biases and detecting regulatory elements in STARR-seq data. Genome Res., 31: 877–889
https://doi.org/10.1101/gr.269209.120
72 T., Jores, J., Tonnies, T., Wrightsman, E. S., Buckler, J. T., Cuperus, S. Fields, (2021). Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters. Nat. Plants, 7: 842–855
https://doi.org/10.1038/s41477-021-00932-y
73 J., Sun, N., He, L., Niu, Y., Huang, W., Shen, Y., Zhang, L. Li, (2019). Global quantitative mapping of enhancers in rice by STARR-seq. Genom. Proteom. Bioinf., 17: 140–153
https://doi.org/10.1016/j.gpb.2018.11.003
74 C., TianY., ZhangJ. Li. (2022) Benchmarking intrinsic promoters and terminators for plant synthetic biology research. BioDesign Research., 2022
75 A. I., AndreouJ., NirkkoM. Ochoa-Villarreal. (2021) Mobius assembly for plant systems highlights promoter-terminator interaction in gene regulation. bioRxiv doi: 10.1101/2021.03.31.437819
76 A., Gunadi, P. J., Rushton, L. K., Mchale, A. H. Gutek, J. Finer, (2016). Characterization of 40 soybean (Glycine max) promoters, isolated from across 5 thematic gene groups. Plant Cell Tissue Organ Cult., 127: 1–16
https://doi.org/10.1007/s11240-016-1038-x
77 Y., Kakei, H., Masuda, N. K., Nishizawa, H. Hattori, M. Aung, (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
https://doi.org/10.3389/fpls.2021.660303
78 A., Kaur, P. K., Pati, A. M. Pati, A. Nagpal, (2017). In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa. PLoS One, 12: e0184523
https://doi.org/10.1371/journal.pone.0184523
79 R. J., Schmitz, E. Grotewold, (2022). Cis-regulatory sequences in plants: their importance, discovery, and future challenges. Plant Cell, 34: 718–741
https://doi.org/10.1093/plcell/koab281
80 D. Basu, P. South, (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
https://doi.org/10.3389/fpls.2022.828729
81 J. P. C., To, I. W., Davis, M. S., Marengo, A., Shariff, C., Baublite, K., Decker, Z., Gao, O., Haragutchi, J. W. Jung, et al.. (2021). Expression elements derived from plant sequences provide effective gene expression regulation and new opportunities for plant biotechnology traits. Front. Plant Sci., 12: 712179
https://doi.org/10.3389/fpls.2021.712179
82 I. W., Davis, C., Benninger, P. N. Benfey, (2012). POWRS: position-sensitive motif discovery. PLoS One, 7: e40373
https://doi.org/10.1371/journal.pone.0040373
83 D. M. McCarthy, J. Medford, (2020). Quantitative and predictive genetic parts for plant synthetic biology. Front. Plant Sci., 11: 512526
https://doi.org/10.3389/fpls.2020.512526
84 L., Han, S., Silvestre, O., Sayanova, R. P. Haslam, J. Napier, (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
https://doi.org/10.1111/pbi.13867
85 J. R., Petrie, X. R., Zhou, A., Leonforte, J., McAllister, P., Shrestha, Y., Kennedy, S., Belide, G., Buzza, N., Gororo, W. Gao, et al.. (2020). Development of a Brassica napus (Canola) crop containing fish oil-like levels of DHA in the seed oil. Front. Plant Sci., 11: 727
https://doi.org/10.3389/fpls.2020.00727
86 S., Belide, P., Shrestha, Y., Kennedy, A., Leonforte, M. D., Devine, J. R., Petrie, S. P. Singh, X. Zhou, (2022). Engineering docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA) in Brassica juncea. Plant Biotechnol. J., 20: 19–21
https://doi.org/10.1111/pbi.13739
87 L., Yan-yan, G., Li-na, L. Cheng-zhen, M. Zhi-gang, Y., Li, L., Guo, C., Liang, Z., Meng, S., Tahira, S. Guo, (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 H., Fan, Y., Liu, C. Y., Li, Y., Jiang, J. J., Song, L., Yang, Q., Zhao, Y. H., Hu, X. Y. Chen, J. Xu, (2021). Engineering high coenzyme Q10 tomato. Metab. Eng., 68: 86–93
https://doi.org/10.1016/j.ymben.2021.09.007
89 E. C. F., Forestier, T., Czechowski, A. C., Cording, A. D., Gilday, A. J., King, G. D. Brown, I. Graham, (2021). Developing a Nicotiana benthamiana transgenic platform for high-value diterpene production and candidate gene evaluation. Plant Biotechnol. J., 19: 1614–1623
https://doi.org/10.1111/pbi.13574
90 K., Davis, D. S., Gkotsi, D. R. M., Smith, R. J. M., Goss, L. Caputi, S. Connor, (2020). Nicotiana benthamiana as a transient expression host to produce auxin analogs. Front. Plant Sci., 11: 581675
https://doi.org/10.3389/fpls.2020.581675
91 Q. M., Allen, V. J., Febres, B. Rathinasabapathi, J. Chaparro, (2022). Engineering a plant-derived astaxanthin synthetic pathway into Nicotiana benthamiana. Front. Plant Sci., 12: 831785
https://doi.org/10.3389/fpls.2021.831785
92 N., Narayanan, G., Beyene, R. D., Chauhan, J., Gehan, P., Butts, D., Siritunga, I., Okwuonu, A., Woll, D. M. nez-Aguilar, et al.. (2019). Biofortification of field-grown cassava by engineering expression of an iron transporter and ferritin. Nat. Biotechnol., 37: 144–151
https://doi.org/10.1038/s41587-018-0002-1
93 Q., Liang, K., Wang, X., Liu, B., Riaz, L., Jiang, X., Wan, X. Ye, (2019). Improved folate accumulation in genetically modified maize and wheat. J. Exp. Bot., 70: 1539–1551
https://doi.org/10.1093/jxb/ery453
94 X., Liu, X., Ma, H., Wang, S., Li, W., Yang, R. D., Nugroho, L., Luo, X., Zhou, C., Tang, Y. Fan, et al.. (2021). Metabolic engineering of astaxanthin-rich maize and its use in the production of biofortified eggs. Plant Biotechnol. J., 19: 1812–1823
https://doi.org/10.1111/pbi.13593
95 R. S., Nett, W. Lau, E. Sattely, (2020). Discovery and engineering of colchicine alkaloid biosynthesis. Nature, 584: 148–153
https://doi.org/10.1038/s41586-020-2546-8
96 J., Li, I., Mutanda, K., Wang, L., Yang, J. Wang, (2019). Chloroplastic metabolic engineering coupled with isoprenoid pool enhancement for committed taxanes biosynthesis in Nicotiana benthamiana. Nat. Commun., 10: 4850
https://doi.org/10.1038/s41467-019-12879-y
97 R., ndez, E., nez, S., Gianoglio, A., Quijano-Rubio, A., Rubert, J. L., Rambla, M., Vazquez-Vilar, E. Huet, et al.. (2021). Production of volatile moth sex pheromones in transgenic Nicotiana benthamiana plants. BioDesign Research., 2021: 9891082
https://doi.org/10.34133/2021/9891082
98 S., Iacopino, S., Jurinovich, L., Cupellini, L., Piccinini, F., Cardarelli, P., Perata, B., Mennucci, B. Giuntoli, (2019). A synthetic oxygen sensor for plants based on animal hypoxia signaling. Plant Physiol., 179: 986–1000
https://doi.org/10.1104/pp.18.01003
99 J. L. Nemhauser, K. Torii, (2016). Plant synthetic biology for molecular engineering of signalling and development. Nat. Plants, 2: 16010
https://doi.org/10.1038/nplants.2016.10
100 G., Swinnen, A. Goossens, (2016). Lessons from domestication: targeting cis-regulatory elements for crop improvement. Trends Plant Sci., 21: 506–515
https://doi.org/10.1016/j.tplants.2016.01.014
101 J., Andres, T. Blomeier, M. Zurbriggen, (2019). Synthetic switches and regulatory circuits in plants. Plant Physiol., 179: 862–884
https://doi.org/10.1104/pp.18.01362
102 Y., Zhou, M., Ding, S., Gao, J., Yu-Strzelczyk, M., Krischke, X., Duan, J., Leide, M., Riederer, M. J., Mueller, R. Hedrich, et al.. (2021). Optogenetic control of plant growth by a microbial rhodopsin. Nat. Plants, 7: 144–151
https://doi.org/10.1038/s41477-021-00853-w
103 L., Liu, J., Gallagher, E. D., Arevalo, R., Chen, T., Skopelitis, Q., Wu, M. Bartlett, (2021). Enhancing grain-yield-related traits by CRISPR-Cas9 promoter editing of maize CLE genes. Nat. Plants, 7: 287–294
https://doi.org/10.1038/s41477-021-00858-5
104 D., guez-Leal, Z. H., Lemmon, J., Man, M. E. Bartlett, Z. Lippman, (2017). Engineering quantitative trait variation for crop improvement by genome editing. Cell, 171: 470–480.e8
https://doi.org/10.1016/j.cell.2017.08.030
105 X., Song, X., Meng, H., Guo, Q., Cheng, Y., Jing, M., Chen, G., Liu, B., Wang, Y., Wang, J. Li, et al.. (2022). Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nat. Biotechnol., 40: 1403–1411
https://doi.org/10.1038/s41587-022-01281-7
106 L. G., Lowder, J., Zhou, Y., Zhang, A., Malzahn, Z., Zhong, T. F., Hsieh, D. F., Voytas, Y. Zhang, (2018). Robust transcriptional activation in plants using multiplexed CRISPR-Act2. 0 and mTALE-Act systems. Mol. Plant, 11: 245–256
https://doi.org/10.1016/j.molp.2017.11.010
107 L. G., Lowder, J. W. Paul, (2017). Multiplexed transcriptional activation or repression in plants using CRISPR-dCas9-based systems. Methods Mol. Biol., 1629: 167–184
https://doi.org/10.1007/978-1-4939-7125-1_12
108 L. G., Lowder, D., Zhang, N. J., Baltes, J. W. Paul, X., Tang, X., Zheng, D. F., Voytas, T. F., Hsieh, Y. Zhang, (2015). A CRISPR/Cas9 toolbox for multiplexed plant genome editing and transcriptional regulation. Plant Physiol., 169: 971–985
https://doi.org/10.1104/pp.15.00636
109 A., Chavez, J., Scheiman, S., Vora, B. W., Pruitt, M., Tuttle, E., P R Iyer, S., Lin, S., Kiani, C. D., Guzman, D. J. Wiegand, et al.. (2015). Highly efficient Cas9-mediated transcriptional programming. Nat. Methods, 12: 326–328
https://doi.org/10.1038/nmeth.3312
110 S. Sajwan, (2019). Gene activation by dCas9-CBP and the SAM system differ in target preference. Sci. Rep., 9: 18104
https://doi.org/10.1038/s41598-019-54179-x
111 J. G., Zalatan, M. E., Lee, R., Almeida, L. A., Gilbert, E. H., Whitehead, M., La Russa, J. C., Tsai, J. S., Weissman, J. E., Dueber, L. S. Qi, et al.. (2015). Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell, 160: 339–350
https://doi.org/10.1016/j.cell.2014.11.052
112 H., Zhou, J., Liu, C., Zhou, N., Gao, Z., Rao, H., Li, X., Hu, C., Li, X., Yao, X. Shen, et al.. (2018). In vivo simultaneous transcriptional activation of multiple genes in the brain using CRISPR-dCas9-activator transgenic mice. Nat. Neurosci., 21: 440–446
https://doi.org/10.1038/s41593-017-0060-6
113 Z., Li, D., Zhang, X., Xiong, B., Yan, W., Xie, J. Sheen, J. Li, (2017). A potent Cas9-derived gene activator for plant and mammalian cells. Nat. Plants, 3: 930–936
https://doi.org/10.1038/s41477-017-0046-0
114 C., Pan, X., Wu, K., Markel, A. A., Malzahn, N., Kundagrami, S., Sretenovic, Y., Zhang, Y., Cheng, P. M. Shih, (2021). CRISPR-Act3. 0 for highly efficient multiplexed gene activation in plants. Nat. Plants, 7: 942–953
https://doi.org/10.1038/s41477-021-00953-7
115 S., Selma, J. M., -Orts, M., Vazquez-Vilar, B., Diego-Martin, M., Ajenjo, V., Garcia-Carpintero, A. Granell, (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
https://doi.org/10.1111/pbi.13138
116 S., Selma, A., Espinosa-Ruiz, S., Gianoglio, M. P., Lopez-Gresa, M., zquez-Vilar, V., Flors, A. Granell, (2022). Custom-made design of metabolite composition in N. benthamiana leaves using CRISPR activators. Plant Biotechnol. J., 20: 1578–1590
https://doi.org/10.1111/pbi.13834
117 C., Pan, G., Li, A. A., Malzahn, Y., Cheng, B., Leyson, S., Sretenovic, F., Gurel, G. D. Coleman, (2022). Boosting plant genome editing with a versatile CRISPR-Combo system. Nat. Plants, 8: 513–525
https://doi.org/10.1038/s41477-022-01151-9
118 N., Dey, S., Sarkar, S. Acharya, I. Maiti, (2015). Synthetic promoters in planta. Planta, 242: 1077–1094
https://doi.org/10.1007/s00425-015-2377-2
119 J., Aysha, M., Noman, F., Wang, W., Liu, Y., Zhou, H. Li, (2018). Synthetic promoters: designing the cis regulatory modules for controlled gene expression. Mol. Biotechnol., 60: 608–620
https://doi.org/10.1007/s12033-018-0089-0
120 R. Pandiarajan. (2018) In vivo promoter engineering in plants: are we ready? Plant Sci., 277, 132–138
121 W. Z., Ouma, K. Pogacar, (2018). Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. PLOS Comput. Biol., 14: e1006098
https://doi.org/10.1371/journal.pcbi.1006098
122 L. J., Core, A. L., Martins, C. G., Danko, C. T., Waters, A. Siepel, J. Lis, (2014). Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat. Genet., 46: 1311–1320
https://doi.org/10.1038/ng.3142
123 W. A., Ricci, Z., Lu, L., Ji, A. P., Marand, C. L., Ethridge, N. G., Murphy, J. M., Noshay, M., Galli, M. K., a-Guerra, et al.. (2019). Widespread long-range cis-regulatory elements in the maize genome. Nat. Plants, 5: 1237–1249
https://doi.org/10.1038/s41477-019-0547-0
124 M., Fagny, M. L., Kuijjer, M., Stam, J., Joets, O., Turc, J., re, S., Pateyron, A. Venon, (2021). Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks. Front. Genet., 11: 606285
https://doi.org/10.3389/fgene.2020.606285
125 W., Ding, J., Cheng, D., Guo, L., Mao, J., Li, L., Lu, Y., Zhang, J. Yang, (2018). Engineering the 5′UTR-mediated regulation of protein abundance in yeast using nucleotide sequence activity relationships. ACS Synth. Biol., 7: 2709–2714
https://doi.org/10.1021/acssynbio.8b00127
126 V. K., Mutalik, J. C., Guimaraes, G., Cambray, C., Lam, M. J., Christoffersen, Q. A., Mai, A. B., Tran, M., Paull, J. D., Keasling, A. P. Arkin, et al.. (2013). Precise and reliable gene expression via standard transcription and translation initiation elements. Nat. Methods, 10: 354–360
https://doi.org/10.1038/nmeth.2404
127 Y., Chen, S., Zhang, E. M., Young, T. S., Jones, D. Densmore, C. Voigt, (2020). Genetic circuit design automation for yeast. Nat. Microbiol., 5: 1349–1360
https://doi.org/10.1038/s41564-020-0757-2
128 P. F., Xia, H., Ling, J. L. Foo, M. Chang, (2019). Synthetic genetic circuits for programmable biological functionalities. Biotechnol. Adv., 37: 107393
https://doi.org/10.1016/j.biotechadv.2019.04.015
129 M., Van Brempt, J., Clauwaert, F., Mey, M., Stock, J., Maertens, W. Waegeman, (2020). Predictive design of sigma factor-specific promoters. Nat. Commun., 11: 5822
https://doi.org/10.1038/s41467-020-19446-w
130 A. A., Nielsen, B. S., Der, J., Shin, P., Vaidyanathan, V., Paralanov, E. A., Strychalski, D., Ross, D. Densmore, C. Voigt, (2016). Genetic circuit design automation. Science, 352: aac7341
https://doi.org/10.1126/science.aac7341
131 T. S., Jones, S. M. D., Oliveira, C. J., Myers, C. A. Voigt, (2022). Genetic circuit design automation with Cello 2. 0. Nat. Protoc., 17: 1097–1113
https://doi.org/10.1038/s41596-021-00675-2
132 R. Kwok, (2010). Five hard truths for synthetic biology. Nature, 463: 288–290
https://doi.org/10.1038/463288a
133 J., Shin, S., Zhang, B. S., Der, A. A. Nielsen, C. Voigt, (2020). Programming Escherichia coli to function as a digital display. Mol. Syst. Biol., 16: e9401
https://doi.org/10.15252/msb.20199401
134 M., Crowther, A. Wipat, (2022). A network approach to genetic circuit designs. ACS Synth. Biol., 11: 3058–3066
https://doi.org/10.1021/acssynbio.2c00255
135 A. lez, (2019). Benefits of using genomic insulators flanking transgenes to increase expression and avoid positional effects. Sci. Rep., 9: 8474
https://doi.org/10.1038/s41598-019-44836-6
136 H., Puchta, J., Jiang, K. Wang, (2022). Updates on gene editing and its applications. Plant Physiol., 188: 1725–1730
https://doi.org/10.1093/plphys/kiac032
137 B. M., Neill, K. L., Mikkelson, N. M., Gutierrez, J. L., Cunningham, K. L., Wolff, S. J., Szyjka, C. B., Yohn, K. E. Redding, M. Mendez, (2012). An exogenous chloroplast genome for complex sequence manipulation in algae. Nucleic Acids Res., 40: 2782–2792
https://doi.org/10.1093/nar/gkr1008
138 Y., Shao, N., Lu, Z., Wu, C., Cai, S., Wang, L. L., Zhang, F., Zhou, S., Xiao, L., Liu, X. Zeng, et al.. (2018). Creating a functional single-chromosome yeast. Nature, 560: 331–335
https://doi.org/10.1038/s41586-018-0382-x
139 M. Tomita, (2001). Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol., 19: 205–210
https://doi.org/10.1016/S0167-7799(01)01636-5
140 J. R., Karr, J. C., Sanghvi, D. N., Macklin, M. V., Gutschow, J. M., Jacobs, B. Bolival, N., Assad-Garcia, J. I. Glass, M. Covert, (2012). A whole-cell computational model predicts phenotype from genotype. Cell, 150: 389–401
https://doi.org/10.1016/j.cell.2012.05.044
141 D. N., Macklin, T. A., Ahn-Horst, H., Choi, N. A., Ruggero, J., Carrera, J. C., Mason, G., Sun, E., Agmon, M. M., DeFelice, I. Maayan, et al.. (2020). Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science, 369: eaav3751
https://doi.org/10.1126/science.aav3751
142 M., Maritan, L., Autin, J., Karr, M. W., Covert, A. J. Olson, D. Goodsell, (2022). Building structural models of a whole mycoplasma cell. J. Mol. Biol., 434: 167351
https://doi.org/10.1016/j.jmb.2021.167351
143 H., Lu, E. J. Kerkhoven, (2022). Multiscale models quantifying yeast physiology: towards a whole-cell model. Trends Biotechnol., 40: 291–305
https://doi.org/10.1016/j.tibtech.2021.06.010
144 D. A., Beard, M. L., Neal, N., Tabesh-Saleki, C. T., Thompson, J. B., Bassingthwaighte, M. Shimoyama, B. Carlson, (2012). Multiscale modeling and data integration in the virtual physiological rat project. Ann. Biomed. Eng., 40: 2365–2378
https://doi.org/10.1007/s10439-012-0611-7
145 A., Marshall-Colon, S. P., Long, D. K., Allen, G., Allen, D. A., Beard, B., Benes, S., von Caemmerer, A. J., Christensen, D. J., Cox, J. C. Hart, et al.. (2017). Crops in silico: generating virtual crops using an integrative and multi-scale modeling platform. Front. Plant Sci., 8: 786
https://doi.org/10.3389/fpls.2017.00786
146 X. G., Zhu, J. P., Lynch, D. S., LeBauer, A. J., Millar, M. Stitt, S. Long, (2016). Plants in silico: why, why now and what?—an integrative platform for plant systems biology research. Plant Cell Environ., 39: 1049–1057
https://doi.org/10.1111/pce.12673
147 H., Zheng, P. Y., Ho, M., Jiang, B., Tang, W., Liu, D., Li, X., Yu, N. E., Kleckner, A. Amir, (2016). Interrogating the Escherichia coli cell cycle by cell dimension perturbations. Proc. Natl. Acad. Sci. USA, 113: 15000–15005
https://doi.org/10.1073/pnas.1617932114
148 P., Du, H., Zhao, H., Zhang, R., Wang, J., Huang, Y., Tian, X., Luo, X., Luo, M., Wang, Y. Xiang, et al.. (2020). De novo design of an intercellular signaling toolbox for multi-channel cell-cell communication and biological computation. Nat. Commun., 11: 4226
https://doi.org/10.1038/s41467-020-17993-w
149 A., Milias-Argeitis, M., Rullan, S. K., Aoki, P. Buchmann, (2016). Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat. Commun., 7: 12546
https://doi.org/10.1038/ncomms12546
150 E. J., Olson, L. A., Hartsough, B. P., Landry, R. Shroff, J. Tabor, (2014). Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals. Nat. Methods, 11: 449–455
https://doi.org/10.1038/nmeth.2884
151 A., Carignano, D. H., Chen, C., Mallory, R. C., Wright, G. Seelig, (2022). Modular, robust, and extendible multicellular circuit design in yeast. eLife, 11: e74540
https://doi.org/10.7554/eLife.74540
152 M. W., Dorrity, C. M., Alexandre, M. O., Hamm, A. L., Vigil, S., Fields, C. Queitsch, J. Cuperus, (2021). The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. Nat. Commun., 12: 3334
https://doi.org/10.1038/s41467-021-23675-y
153 A., Farmer, S., Thibivilliers, K. H., Ryu, J. Schiefelbein, (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
https://doi.org/10.1016/j.molp.2021.01.001
154 E., Okubo-Kurihara, A., Ali, M., Hiramoto, Y., Kurihara, Y., Abouleila, E. M., Abdelazem, T., Kawai, Y., Makita, M., Kawashima, T. Esaki, et al.. (2022). Tracking metabolites at single-cell resolution reveals metabolic dynamics during plant mitosis. Plant Physiol., 189: 459–464
https://doi.org/10.1093/plphys/kiac093
155 A. P., Marand, Z., Chen, A. Gallavotti, R. Schmitz, (2021). A cis-regulatory atlas in maize at single-cell resolution. Cell, 184: 3041–3055.e21
https://doi.org/10.1016/j.cell.2021.04.014
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed