Design of synthetic promoters for cyanobacteria with generative deep-learning model
Open accessAbstract
Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, allowing us to design genetic parts with accuracy. Here, we provide a deep-learning based generic framework to design and evaluate synthetic promoters for cyanobacteria using generative models, which was in turn validated with cell-free transcription assay. We developed a deep generative model and a predictive model using a variational autoencoder and convolutional neural network, respectively. Using native promoter sequences of the model unicellular cyanobacterium Synechocystis sp. PCC 6803 as a training dataset, we generated 10 000 synthetic promoter sequences and predicted their strengths. By position weight matrix and k-mer analyses, we confirmed that our model captured a valid feature of cyanobacteria promoters from the dataset. Furthermore, critical subregion identification analysis consistently revealed the importance of the -10 box sequence motif in cyanobacteria promoters. Moreover, we validated that the generated promoter sequence can efficiently drive transcription via cell-free transcription assay. This approach, combining in silico and in vitro studies, will provide a foundation for the rapid design and validation of synthetic promoters, especially for non-model organisms.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Synthetic promoters are especially useful in metabolic engineering contexts where careful adjustment of multiple gene expression levels is necessary for efficient pathway function.
“Synthetic promoters are especially useful in metabolic engineering contexts where careful adjustment of multiple gene expression levels is necessary for efficient pathway function [100,101].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [101]
The Introduction (page unavailable) says fine-tuning gene expression is crucial for engineering and that synthetic promoters can achieve desired transcriptional activity. The Results (page unavailable) show synthetic promoters with varied strengths and discuss promoters used in cyanobacterial metabolic engineering. The paper does not establish the more specific claim that adjusting multiple genes’ expression levels is necessary for efficient pathway function. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
WebLogo: A Sequence Logo Generator: Figure 1Preprint
Crooks, G. E., Hon, G. C., Chandonia, J., & Brenner, S. E. (2004) · Genome Research · doi:10.1101/gr.849004 · Cited by 12,433 · see citing works
Enzymatic assembly of DNA molecules up to several hundred kilobases
Gibson, D. G., Young, L., Chuang, R.-Y., Venter, J. C., Hutchison, C. A., & Smith, H. O. (2009) · Nature Methods · doi:10.1038/nmeth.1318 · Cited by 9,742 · see citing works
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · cited 1× · see citing works
“As a popular artificial neural network model, CNN is widely used for the analysis of images ( 32 , 33 ) or sequence data in a two-dimensional form converted through one-hot encoding ( 16 ).”
CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity
Chen, J. S., Ma, E., Harrington, L. B., Da Costa, M., Tian, X., Palefsky, J. M., & Doudna, J. A. (2018) · Science · doi:10.1126/science.aar6245 · Cited by 4,507 · see citing works
Nucleic acid detection with CRISPR-Cas13a/C2c2
Gootenberg, J. S., Abudayyeh, O. O., Lee, J. W., Essletzbichler, P., Dy, A. J., Joung, J., Verdine, V. K., Donghia, N. M., Daringer, N. M., Freije, C. A., et al. (2017) · Science · doi:10.1126/science.aam9321 · Cited by 3,887 · see citing works
Self-citationDeep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503 · see citing works
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228 · see citing works
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873 · see citing works
Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome
Gibson, D. G., Glass, J. I., Lartigue, C., Noskov, V. N., Chuang, R.-Y., Algire, M. A., Benders, G. A., Montague, M. G., Ma, L., Moodie, M. M., et al. (2010) · Science · doi:10.1126/science.1190719 · Cited by 2,638 · see citing works
Deep Generative Image Models using a Laplacian Pyramid of Adversarial NetworksPreprint
Denton, E., Chintala, S., Szlam, A., & Fergus, R. (2015) · Neural Information Processing Systems · doi:10.48550/arxiv.1506.05751 · Cited by 2,360 · see citing works
A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
Applied bioinformatics for the identification of regulatory elements
Wasserman, W. W., & Sandelin, A. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1315 · Cited by 1,206 · see citing works
The primary transcriptome of the major human pathogen Helicobacter pylori
Sharma, C. M., Hoffmann, S., Darfeuille, F., Reignier, J., Findeiß, S., Sittka, A., Chabas, S., Reiche, K., Jörg Hackermüller, A., Reinhardt, R., et al. (2010) · Nature · doi:10.1038/nature08756 · Cited by 1,133 · see citing works
Webb, S. (2018) · Nature · doi:10.1038/d41586-018-02174-z · Cited by 359 · see citing works
Thomason, M. K., Bischler, T., Eisenbart, S. K., Förstner, K. U., Zhang, A., Herbig, A., Nieselt, K., Sharma, C. M., & Storz, G. (2015) · Journal of Bacteriology · doi:10.1128/jb.02096-14 · Cited by 335 · see citing works
Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers
Ryu, J. Y., Kim, H. U., & Lee, S. Y. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821905116 · Cited by 294 · see citing works
DEEPre: sequence-based enzyme EC number prediction by deep learning
Li, Y., Wang, S., Umarov, R. K., Xie, B., Fan, M., Li, L., & Gao, X. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx680 · Cited by 291 · see citing works
Umarov, R. K., & Solovyev, V. V. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171410 · Cited by 269 · see citing works
An introduction to deep generative modeling
Ruthotto, L., & Haber, E. (2021) · GAMM-Mitteilungen · doi:10.1002/gamm.202100008 · Cited by 269 · see citing works
Griese, M., Lange, C., & Soppa, J. (2011) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2011.02368.x · Cited by 264 · see citing works
In vitro prototyping and rapid optimization of biosynthetic enzymes for cell design
Karim, A. S., Dudley, Q. M., Juminaga, A., Yuan, Y., Crowe, S. A., Heggestad, J. T., Garg, S., Abdalla, T., Grubbe, W. S., Rasor, B. J., et al. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0559-0 · Cited by 244 · see citing works
Comparative Analysis of the Primary Transcriptome of Synechocystis sp. PCC 6803
Kopf, M., Klähn, S., Scholz, I., Matthiessen, J. K. F., Hess, W. R., & Voß, B. (2014) · DNA Research · doi:10.1093/dnares/dsu018 · Cited by 242 · see citing works
Customized optimization of metabolic pathways by combinatorial transcriptional engineering
Du, J., Yuan, Y., Si, T., Lian, J., & Zhao, H. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks549 · Cited by 239 · see citing works
A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
Costello, Z., & Martín, H. G. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0054-3 · Cited by 238 · see citing works
Nevoigt, E., Kohnke, J., Fischer, C. R., Alper, H. S., Ståhl, U., & Stephanopoulos, G. N. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.00530-06 · Cited by 226 · see citing works
Cyanobacteria: Promising biocatalysts for sustainable chemical production
Knoot, C. J., Ungerer, J., Wangikar, P. P., & Pakrasi, H. B. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.r117.815886 · Cited by 225 · see citing works
Synthetic promoter design in Escherichia coli based on a deep generative network
Wang, Y., Wang, H., Wei, L., Li, S., Liu, L., & Wang, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa325 · Cited by 218 · see citing works
A part toolbox to tune genetic expression inBacillus subtilis
Guiziou, S., Sauveplane, V., Chang, H., Clerté, C., Declerck, N., Jules, M., & Bonnet, J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw624 · Cited by 203 · see citing works
Marshall, R., Maxwell, C. S., Collins, S. P., Jacobsen, T., Luo, M. L., Begemann, M. B., Gray, B. N., January, E., Singer, A., He, Y., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2017.12.007 · Cited by 203 · see citing works
Differential RNA-seq: the approach behind and the biological insight gained
Sharma, C. M., & Vogel, J. (2014) · Current Opinion in Microbiology · doi:10.1016/j.mib.2014.06.010 · Cited by 202 · see citing works
Moore, S. J., MacDonald, J. T., Wienecke, S., Ishwarbhai, A., Tsipa, A., Aw, R., Kylilis, N., Bell, D. J., McClymont, D. W., Jensen, K., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715806115 · Cited by 198 · see citing works
Machine learning applications in systems metabolic engineering
Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.08.010 · Cited by 195 · see citing works
DeePromoter: Robust Promoter Predictor Using Deep Learning
Oubounyt, M., Louadi, Z., Tayara, H., & Chong, K. T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00286 · Cited by 192 · see citing works
Englund, E., Liang, F., & Lindberg, P. (2016) · Scientific Reports · doi:10.1038/srep36640 · Cited by 166 · see citing works
Kim, D., Hong, J. S.-J., Qiu, Y. L., Nagarajan, H., Seo, J., Cho, B., Tsai, S., & Palsson, B. Ø. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002867 · Cited by 146 · see citing works
Self-citationModel-driven generation of artificial yeast promoters
Kotopka, B. J., & Smolke, C. D. (2020) · Nature Communications · doi:10.1038/s41467-020-15977-4 · Cited by 143 · see citing works
Protein sequence design with deep generative models
Wu, Z., Johnston, K. E., Arnold, F. H., & Yang, K. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.004 · Cited by 141 · see citing works
Babski, J., Haas, K. A., NäTher-Schindler, D., Pfeiffer, F., Förstner, K. U., Hammelmann, M., Hilker, R., Becker, A., Sharma, C. M., Marchfelder, A., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-2920-y · Cited by 140 · see citing works
On the Quantitative Analysis of Decoder-Based Generative ModelsPreprint
Wu, Y., Burda, Y., Salakhutdinov, R., & Grosse, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04273 · Cited by 133 · see citing works
Promoter analysis and prediction in the human genome using sequence-based deep learning models
Umarov, R. K., Kuwahara, H., Li, Y., Gao, X., & Solovyev, V. V. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1068 · Cited by 118 · see citing works
Synthetic Core Promoters as Universal Parts for Fine-Tuning Expression in Different Yeast Species
Portela, R. M. C., Vogl, T., Kniely, C., Fischer, J. E., Oliveira, R., & Glieder, A. (2016) · ACS Synthetic Biology · doi:10.1021/acssynbio.6b00178 · Cited by 117 · see citing works
A high resolution map of a cyanobacterial transcriptome
Vijayan, V., Jain, I. H., & O’Shea, E. K. (2011) · Genome biology · doi:10.1186/gb-2011-12-5-r47 · Cited by 107 · see citing works
Zerulla, K., Ludt, K., & Soppa, J. (2016) · Microbiology · doi:10.1099/mic.0.000264 · Cited by 106 · see citing works
Liu, D., & Pakrasi, H. B. (2018) · Microbial Cell Factories · doi:10.1186/s12934-018-0897-8 · Cited by 98 · see citing works
Liu, D., Mao, Z., Guo, J., Wei, L., Ma, H., Ya-jie Tang, A., Chen, T., Wang, Z., & Zhao, X. (2018) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00115 · Cited by 97 · see citing works
Santos, E. L. C. D. L., Meyerowitz, J. T., Mayo, S. L., & Murray, R. M. (2015) · ACS Synthetic Biology · doi:10.1021/acssynbio.5b00090 · Cited by 96 · see citing works
Enhancing scientific discoveries in molecular biology with deep generative models
Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82 · see citing works
Ferreira, E. A., Pacheco, C. C., Pinto, F., Pereira, J. A., Lamosa, P., Oliveira, P., Kirov, B., Jaramillo, A., & Tamagnini, P. (2018) · Synthetic Biology · doi:10.1093/synbio/ysy014 · Cited by 59 · see citing works
Synthetic Biology Toolkits for Metabolic Engineering of Cyanobacteria
Xia, P., Ling, H., Foo, J. L., & Chang, M. W. (2019) · Biotechnology Journal · doi:10.1002/biot.201800496 · Cited by 58 · see citing works
Zhao, Y., Liu, S., Lu, Z., Zhao, B., Wang, S., Zhang, C., Xiao, D., Foo, J. L., & Yu, A. (2021) · Biotechnology for Biofuels · doi:10.1186/s13068-021-02002-z · Cited by 54 · see citing works
Deng, J., Wu, Y., Zheng, Z., Chen, N., Luo, X., Tang, H., & Keasling, J. D. (2021) · Microbial Cell Factories · doi:10.1186/s12934-021-01691-3 · Cited by 53 · see citing works
Predictive design of sigma factor-specific promoters
Brempt, M. V., Clauwaert, J., Mey, F., Stock, M., Maertens, J., Waegeman, W., & Mey, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19446-w · Cited by 52 · see citing works
DeepBAR: A Fast and Exact Method for Binding Free Energy Computation
Ding, X., & Zhang, B. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c00189 · Cited by 41 · see citing works
Regulatory Tools for Controlling Gene Expression in Cyanobacteria
Gordon, G. C., & Pfleger, B. F. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-0854-3_12 · Cited by 40 · see citing works
Guazzaroni, M., & Silva‐Rocha, R. (2014) · ACS Synthetic Biology · doi:10.1021/sb500084f · Cited by 36 · see citing works
Acetyl-CoA-derived biofuel and biochemical production in cyanobacteria: a mini review
Choi, Y., Lee, J. W., Kim, J. W., & Park, J. M. (2020) · Journal of Applied Phycology · doi:10.1007/s10811-020-02128-x · Cited by 33 · see citing works
Self-citationAn Improved Promoter Recognition Model Using Convolutional Neural Network
Qian, Y., Zhang, Y., Guo, B., Ye, S., Wu, Y., & Zhang, J. (2018) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2018.00072 · Cited by 22 · see citing works
Cell-Free Transcription-Coupled CRISPR/Cas12a Assay for Prototyping Cyanobacterial Promoters
Choi, Y., Shin, Y. R., Park, J. M., & Lee, J. W. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00148 · Cited by 12 · see citing works
Self-citationComparative Study of GAN and VAE
Jaydeep, T. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018918039 · Cited by 9 · see citing works
Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb
Liebal, U. W., Köbbing, S., Netze, L., Schweidtmann, A. M., Mitsos, A., & Blank, L. M. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.747428 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [12]Qian Y., Zhang Y., Guo B., Ye S., Wu Y., Zhang J. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). 2018; IEEE 471– 476.
Cited by
Sorted by citations
Diankristanti, P. A., Ho, N. H. E., Chen, J.-H., Nagarajan, D., Chen, C., Hsieh, Y., Ng, I., & Chang, J. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.151124 · Cited by 57 · see citing works
Cui, J., Sun, H., Chen, R., Sun, J., Mo, G., Luan, G., & Lü, X. (2023) · Green Carbon · doi:10.1016/j.greenca.2023.11.004 · Cited by 48 · see citing works
Enabling pathway design by multiplex experimentation and machine learning
Boob, A. G., Chen, J., & Zhao, H. (2023) · Metabolic Engineering · doi:10.1016/j.ymben.2023.11.006 · Cited by 41 · see citing works
Synthetic Promoters in Gene Therapy: Design Approaches, Features and Applications
Artemyev, V., Gubaeva, A., Paremskaia, A. I., Dzhioeva, A. A., Deviatkin, A. A., Feoktistova, S. G., Mityaeva, O. N., & Volchkov, P. (2024) · Cells · doi:10.3390/cells13231963 · Cited by 33 · see citing works
Xia, Y., Du, X., Liu, B., Guo, S., & Huo, Y.-X. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae429 · Cited by 31 · see citing works
Accelerating promoter identification and design by deep learning
Wang, X., Xu, K., Huang, Z., Lin, Y., Zhou, J., Zhou, L., & Ma, F. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.05.008 · Cited by 27 · see citing works
Microbial Technologies Enhanced by Artificial Intelligence for Healthcare Applications
Yu, T., Chae, M., Wang, Z., Ryu, G. U., Kim, G. B., & Lee, S. Y. (2025) · Microbial Biotechnology · doi:10.1111/1751-7915.70131 · Cited by 26 · see citing works
Zhou, X., Feng, R., Ding, N., Cao, W., Liu, Y., Zhou, S., & Deng, Y. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf863 · Cited by 23 · see citing works
Cell-free systems: A synthetic biology tool for rapid prototyping in metabolic engineering
Jeung, K., Kim, M., Jang, E., Shon, Y. J., & Jung, G. Y. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108522 · Cited by 21 · see citing works
Application of Cyanobacteria as Chassis Cells in Synthetic Biology
Liu, X., Tang, K., & Hu, J. (2024) · Microorganisms · doi:10.3390/microorganisms12071375 · Cited by 20 · see citing works
Cell-Free Systems to Mimic and Expand Metabolism
Rasor, B. J., & Erb, T. J. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00729 · Cited by 19 · see citing works
Lei, X., Wang, X., Chen, G., Liang, C., Li, Q., Jiang, H., & Xiong, W. (2025) · mSystems · doi:10.1128/msystems.00183-25 · Cited by 15 · see citing works
De novo promoter design method based on deep generative and dynamic evolution algorithm
Gu, Y., Su, J., Xia, J., Wu, P., Wu, H., Su, Y., Wei, P.-J., & Zheng, C.-H. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf833 · Cited by 14 · see citing works
Machine learning-assisted synthetic biology of cyanobacteria and microalgae
Jin, W., Wang, F., Chen, L., & Zhang, W. (2025) · Algal Research · doi:10.1016/j.algal.2025.103911 · Cited by 14 · see citing works
Synthetic promoter design in plants: integration of computational and experimental approaches
Yaschenko, A. E., Alonso, J. M., & Stepanova, A. N. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1768521 · Cited by 12 · see citing works
De novo design of functional nucleic acids of aptamers
Zhang, Z., Jiang, M., He, A., Zhu, Y., Wang, E., Wan, L., Qiu, J., Guo, P., Chen, G., & Han, D. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00965-3 · Cited by 13 · see citing works
Nam, J., Lee, Y., Lee, S., Choi, H., Lee, S. Y., & Yang, D. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108782 · Cited by 11 · see citing works
Lei, R., Jia, J., Qin, L., & Wei, X. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27364 · Cited by 9 · see citing works
CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution
Ren, R., Yu, H., Teng, J., Mao, S., Bian, Z., Tao, Y., & Yau, S. S.-T. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae398 · Cited by 9 · see citing works
De novo design of synthetic microbial genomes
Koster, C. C., Oliveira, H. D. C., van Beveren, F., Houkes, J., van Hooff, J. J. E., Ettema, T. J. G., John van der, O. O. S. T., & Claassens, N. J. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-026-00410-0 · Cited by 8 · see citing works
Towards AI-designed genomes using a variational autoencoder
Dudek, N. K., & Precup, D. (2024) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2024.1457 · Cited by 7 · see citing works
Deep learning and generative artificial intelligence methods in enzyme and cell engineering
Docter, S., David, B., & Gohlke, H. (2025) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2025.103393 · Cited by 7 · see citing works
Promoters in Pichia pastoris: A Toolbox for Fine-Tuned Gene Expression
Arjmand, S. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4063-0_11 · Cited by 7 · see citing works
Artificial intelligence in the food and bioprocess industries: Addressing food security challenges
Ibarra-Muñoz, L. A., Resendiz-Acosta, G. G., Muñoz-García, R., Alvarado-Mata, L. Y., Sosa-Martínez, J. D., Morales‐Oyervides, L., Montañez, J., & Balagurusamy, N. (2025) · Food and Humanity · doi:10.1016/j.foohum.2025.100818 · Cited by 5 · see citing works
Yu, L., Song, X., Wang, G., Xia, Y., Song, Z., Chen, G., Ai, L., & Xiong, Z. (2025) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2025.01.015 · Cited by 5 · see citing works
Optimizing Nucleic Acid Delivery Systems through Barcode Technology
Park, S., Kim, M., & Lee, J. W. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00602 · Cited by 5 · see citing works
Self-citationOptimization of regulatory DNA with active learning
Shen, Y., Kudla, G. R., & Oyarzún, D. A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.09.033 · Cited by 4 · see citing works
Zhou, X., Cao, W., Huang, C., Zhang, X., Zhou, S., & Deng, Y. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108781 · Cited by 4 · see citing works
A comprehensive survey on artificial intelligence for biomolecule design
Yang, G., Li, J., Wang, Q., Wu, L., Chen, Z., Zhao, P.-C., Wang, H., Wang, Q., Wen, B., Song, C., et al. (2026) · The Innovation Life · doi:10.59717/j.xinn-life.2026.100196 · Cited by 5 · see citing works
Controlling gene expression using AI designed Cis-regulatory elements
Xia, Y., & Huo, Y. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108802 · Cited by 4 · see citing works
Advances in Intelligent Design and Optimization Methods for Nucleic Acid Sequences
Yang, T., Han, M., Wen, X., & Zheng, Y. (2025) · Journal of Artificial Intelligence in Bioinformatics · doi:10.62762/jaib.2025.194547 · Cited by 3 · see citing works
iPro-MP: a BERT-based model to predict multiple prokaryotic promoters
Su, W., Yang, Y. R., Zhao, Y., Yuan, S., Xie, X., Hao, Y., Zhang, H., Ye, D.-X., Lv, H., & Lin, H. (2025) · Genome biology · doi:10.1186/s13059-025-03819-9 · Cited by 3 · see citing works
PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes
Li, C., Zhang, Q., Chen, H., Lin, K., Luo, C., Yang, M., Xu, W., Yao, F., Yan, J., Yang, Q., et al. (2026) · Advanced Science · doi:10.1002/advs.75772 · Cited by 6 · see citing works
Zhou, Q., Wang, Y., Dong, J., Zhao, X., Guo, A. J. X., Wang, X., & Luo, Y. (2026) · Metabolic Engineering · doi:10.1016/j.ymben.2026.05.009 · Cited by 3 · see citing works
Synthetic tunable promoters for flexible control of multi-gene expression in mammalian cells
Fu, Z., Cheng, S., Li, J.-W., Zhang, N., Wu, Y., & Zhao, G.-R. (2025) · Journal of Advanced Research · doi:10.1016/j.jare.2025.02.008 · Cited by 3 · see citing works
Kuhajda, L., Honzik, T., Švec, J., & Georgiev, D. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00684-5 · Cited by 2 · see citing works
Vibrio natriegens, a Promising Chassis from Strain Property to Protein Expression: A Review.
Bao, Y., Li, J.-C., Peng, X., You, S.-P., Qi, W., & Wang, M.-F. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00761 · Cited by 2 · see citing works
Liang, M., Li, C., Zhang, W.-P., & Wang, D. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.134833 · Cited by 2 · see citing works
Engineering regulatory networks of cyanobacteria
Bongirwar, R., & Shukla, P. (2024) · Trends in biotechnology · doi:10.1016/j.tibtech.2023.12.012 · Cited by 2 · see citing works
Walshe, J., & Saha, S. K. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48060638 · Cited by 1 · see citing works
“Synthetic promoters are especially useful in metabolic engineering contexts where careful adjustment of multiple gene expression levels is necessary for efficient pathway function [100,101].”
Generating information-dense promoter sequences with optimal string packing
Andreani, V., South, E. J., & Dunlop, M. J. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012276 · Cited by 1 · see citing works
Yang, W., Sun, Q., Zhang, Y., Yang, J., Gao, F., Cheng, C., & Sun, Y. (2025) · hLife · doi:10.1016/j.hlife.2025.05.009 · Cited by 1 · see citing works
Artificial Intelligence in Medicinal Herb Breeding
Hou, B., Liang, C., Sheng, X. Q., Liu, Y., Ren, J., Ma, Q., Wang, T., & Zhang, L. (2025) · Engineering · doi:10.1016/j.eng.2025.08.021 · Cited by 2 · see citing works
Directed optimization and generation of yeast promoter sequences driven by deep learning
Wang, C., Li, S., Guo, X., Geng, S., Qu, K., Li, M., Wang, C., Zhou, X., Ren, W., Pan, S., et al. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.148505 · Cited by 1 · see citing works
Rachel Veronica, R., Shah, P. B., & Krishnan, M. E. G. (2026) · Bioresource Technology Reports · doi:10.1016/j.biteb.2026.102563 · Cited by 1 · see citing works
Zhou, X., Ding, N., Zhou, S., & Deng, Y. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.5c00765 · Cited by 1 · see citing works
Genetic Modifications in Cyanobacteria: Current Status and Future Prospects
Tripathi, K., & Mishra, Y. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1014041 · Cited by 1 · see citing works
In-silico-driven SynCom engineering with cyanobacteria for efficient pesticide degradation: A review
Raj, A., & Shahid, M. (2026) · Algal Research · doi:10.1016/j.algal.2026.104636 · Cited by 1 · see citing works
Fine-Tuning Gene Expression in Bacteria by Synthetic Promoters
Okay, S. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4063-0_12 · Cited by 1 · see citing works
Shaberi, H. S. A., Bunawan, H., Craig, S., Bryan, S. J., & Ramzi, A. B. (2024) · Algal Research · doi:10.1016/j.algal.2024.103755 · Cited by 1 · see citing works
Mager, M., Becker, L., Schulten, N., Fraune, S., & Axmann, I. M. I. (2025) · Synthetic Biology · doi:10.1093/synbio/ysaf001 · Cited by 1 · see citing works
Language model generates cis- regulatory elements across prokaryotesPreprint
Xia, Y., Sun, J., Du, X., Liang, Z., Shi, W., Guo, S., & Huo, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.07.622410 · Cited by 1 · see citing works
Design prokaryotic cis-regulatory elements using language model
Xia, Y., Sun, J., Du, X., Liang, Z., Wu, X., Shi, W., Shao, B., Guo, S., & Huo, Y. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag122 · Cited by 1 · see citing works
Lian, X.-D., Lee, J., Kang, S., Lee, J., Guan, Y., Lee, M.-K., Jung, K., & Li, Z. (2026) · Algal Research · doi:10.1016/j.algal.2026.104663
Self-citationTang, Z., Wang, S., Jiang, L., Li, D., & Zhu, G. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.122523
Bang, I., Kim, J., Jang, M., Park, J. Y., & Kim, D. (2026) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2026.103505
Self-citationTrieu, K., Bishé, B., Taton, A., Tieu, B. P., & Golden, J. W. (2026) · Applied and Environmental Microbiology · doi:10.1128/aem.00598-26
Deep generative models in biological sequence and structure analysis and design
Palizban, F., Sarbishegi, M. M., Qu, H., Saeidian, A. H., Mehrmohamadi, M., Glessner, J., & Hakonarson, H. H. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.109039
Designing gene expression with MPRA and AI: A new paradigm for synthetic and therapeutic regulation
Zhang, X., Huang, X.-Y., Wang, H.-Y., Bai, F., Li, K., & Hu, S. S. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.109049
A Hybrid Large Language Model and Diffusion Framework for Rational Promoter Design
Lei, X., Wang, X., Liu, Y. T., Zhang, Y., Huang, J., Peng, T., Liang, C., Li, Q., Jiang, H., & Xiong, W. (2026) · ACS Synthetic Biology · doi:10.1021/acssynbio.6c00128
DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding
Xu, F., Fu, R., Cheng, Q., Jiang, S., Yan, J., & Wang, X. (2026) · Science China Life Sciences · doi:10.1007/s11427-025-3406-3 · Cited by 1 · see citing works
DNA-BFN: Bayesian Flow Networks for Generative Modeling of Regulatory DNA Sequences
Yu, H., Du, Q., Wei, L., & Wang, X. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag785
- Authors
- Seo, E., Choi, Y.-N., Shin, Y. R., Kim, D., & Lee, J. W.
- Affiliations
- Pohang University of Science and Technology (KR)
- Ulsan National Institute of Science and Technology (KR)
- Corresponding author
- Lee, Jeong Wook
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-05-29
- Volume
- 51
- Issue
- 13
- Pages
- 7071-7082
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC10359606
- OpenAlex
- W4378640312
- Cited by
- 61
- References
- 63
- Citations by year
- 2026: 24 · 2025: 21 · 2024: 14 · 2023: 2
- FWCI
- 5.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- Bio & Medical Technology Development Program of the National Research Foundation
- Ministry of Science & ICT (2021M3A9I4030408, 2022M3A9I5020804, 2021M3A9I4024840)
- C1 Gas Refinery Program
- MSIT (2015M3D3A1A01064926)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- synthetic promoters, cyanobacteria, Synechocystis sp. PCC 6803, deep generative models, variational autoencoder, convolutional neural networks, cell-free transcription, position weight matrices, k-mer analysis, synthetic biology design, non-model organisms
- MeSH terms
- Deep Learning, Promoter Regions, Genetic, Neural Networks, Computer, Synechocystis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
