AI-Powered Synthetic Biology: Current Situation, Challenges, and Future Perspectives
References
- Abdelrady, W. A., Mostafa, K., Saeed, M., Elshawy, E. E., Kavas, M., Mladenov, V., Bacu, A., & Zeng, F. (2025). NHX transporters: Molecular mechanisms and applications for enhancing crop resilience to soil salinity in changing environments. Plant Physiology and Biochemistry, 229(Pt C), 110603. https://doi.org/10.1016/J.PLAPHY.2025.110603
- Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., Bodenstein, S. W., Evans, D. A., Hung, C. C., O’Neill, M., Reiman, D., Tunyasuvunakool, K., Wu, Z., Žemgulytė, A., Arvaniti, E., … Jumper, J. M. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016), 493–500. https://doi.org/10.1038/s41586-024-07487-w
- Adam, D. (2026). The AI co-scientist is here. Nature Medicine, 32(3), 772–775. https://doi.org/10.1038/S41591-026-04275-Z;KWRD
- Agho, C., Avni, A., Bacu, A., Balazadeh, S., Baloch, F. S., Bazakos, C., Čereković, N., Chaturvedi, P., Chauhan, H., Smet, I. De, Dresselhaus, T., Ferreira, L. J., Fíla, J., Fortes, A. M., Fotopoulos, V., Francesca, S., García-Perez, P., Gong, W., Graci, S., … Fragkostefanakis, S. (2025). Integrative approaches to enhance reproductive resilience of crops for climate-proof agriculture. Plant Stress, 15, 100704. https://doi.org/10.1016/J.STRESS.2024.100704
- Ammar, M., Samsonov, M., Gurylina, E., & Bayzigitov, D. (2026). Artificial intelligence advancements in monoclonal antibody development technology. Frontiers in Immunology, 17, 1802038. https://doi.org/10.3389/FIMMU.2026.1802038
- Anishchenko, I., Pellock, S. J., Chidyausiku, T. M., Ramelot, T. A., Ovchinnikov, S., Hao, J., Bafna, K., Norn, C., Kang, A., Bera, A. K., DiMaio, F., Carter, L., Chow, C. M., Montelione, G. T., & Baker, D. (2021). De novo protein design by deep network hallucination. Nature, 600(7889), 547–552. https://doi.org/10.1038/s41586-021-04184-w
- Aravind Paleri, V., & Hens, K. (2026). Imagining an ethics for synthetic biology. Frontiers in Genetics, 17, 1746379. https://doi.org/10.3389/fgene.2026.1746379
- Athanasopoulou, K., Michalopoulou, V. I., Scorilas, A., & Adamopoulos, P. G. (2025). Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions. Current Issues in Molecular Biology, 47(6), 470. https://doi.org/10.3390/CIMB47060470
- Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods 2021 18:10, 18(10), 1196–1203. https://doi.org/10.1038/s41592-021-01252-x
- Bangi, M. S. F., Kao, K., & Kwon, J. S. Il. (2022). Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae. Chemical Engineering Research & Design, 179, 415–423. https://doi.org/10.1016/J.CHERD.2022.01.041
- Basarali, M. K., Daemi, A., Tahiraga, R. G., Özbolat, G., Hooshiar, M. H., Shirazi, M. S. R., & Döğüş, Y. (2025). Artificial intelligence-driven epigenetic CRISPR therapeutics: a structured multi-domain meta-analysis of therapeutic efficacy, off-target prediction, and gRNA optimization. Functional & Integrative Genomics, 25(1), 223. https://doi.org/10.1007/S10142-025-01725-8
- Bazakos, C., Vidović, M., Radanović, A., Bacu, A., Francesca, S., & Rigano, M. M. (2026). Multi-Level Approaches for Assessing Molecular and Physiological Traits of Drought and Heat Stress Tolerance in Plant Reproductive Development. Physiologia Plantarum, 178(1), e70760. https://doi.org/10.1111/PPL.70760
- Beardall, W. A. V., Stan, G.-B., & Dunlop, M. J. (2022). Deep Learning Concepts and Applications for Synthetic Biology. GEN Biotechnology, 1(4), 360–371. https://doi.org/10.1089/GENBIO.2022.0017
- Berger, B., Daniels, N. M., & William Yu, Y. (2016). Computational Biology in the 21st Century: Scaling with Compressive Algorithms. Communications of the ACM, 59(8), 72. https://doi.org/10.1145/2957324
- Bhardwaj, A., Kishore, S., & Pandey, D. K. (2022). Artificial Intelligence in Biological Sciences. Life (Basel), 12(9), 1430. https://doi.org/10.3390/life12091430
- Bischoff, D., Walla, B., & Weuster-Botz, D. (2022). Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images. Analytical and Bioanalytical Chemistry, 414(21), 6379–6391. https://doi.org/10.1007/S00216-022-04101-8
- Blasiak, A., Khong, J., & Kee, T. (2020). CURATE. AI: Optimizing Personalized Medicine with Artificial Intelligence. SLAS Technology, 25(2), 95–105. https://doi.org/10.1177/2472630319890316
- Bonanni, D., Litrico, M., Ahmed, W., Morerio, P., Cazzorla, T., Spaccapaniccia, E., Cattani, F., Allegretti, M., Beccari, A. R., Del Bue, A., & Martin, F. (2023). A Deep Learning Approach to Optimize Recombinant Protein Production in Escherichia coli Fermentations. Fermentation, 9(6), 503. https://doi.org/10.3390/FERMENTATION9060503
- Bonetti, G., Donato, K., Medori, M. C., Dhuli, K., Henehan, G., Brown, R., Sieving, P., Sykora, P., Marks, R., Falsini, B., Capodicasa, N., Miertus, S., Lorusso, L., Dondossola, D., Tartaglia, G. M., Cerkez Ergoren, M., Dundar, M., Michelini, S., Malacarne, D., … Bertelli, M. (2023). Human Cloning: Biology, Ethics, and Social Implications. La Clinica Terapeutica, 174(Suppl 2(6)), 230–235. https://doi.org/10.7417/CT.2023.2492
- Brixi, G., Durrant, M. G., Ku, J., Naghipourfar, M., Poli, M., Sun, G., Brockman, G., Chang, D., Fanton, A., Gonzalez, G. A., King, S. H., Li, D. B., Merchant, A. T., Nguyen, E., Ricci-Tam, C., Romero, D. W., Schmok, J. C., Taghibakhshi, A., Vorontsov, A., … Hie, B. L. (2026). Genome modelling and design across all domains of life with Evo 2. Nature 2026 652:8112, 652(8112), 1349–1361. https://doi.org/10.1038/s41586-026-10176-5
- Bürger, V. K., Amann, J., Bui, C. K. T., Fehr, J., & Madai, V. I. (2024). The unmet promise of trustworthy AI in healthcare: why we fail at clinical translation. Frontiers in Digital Health, 6, 1279629. https://doi.org/10.3389/FDGTH.2024.1279629
- Busch, F., Hoffmann, L., Rueger, C., van Dijk, E. H. C., Kader, R., Ortiz-Prado, E., Makowski, M. R., Saba, L., Hadamitzky, M., Kather, J. N., Truhn, D., Cuocolo, R., Adams, L. C., & Bressem, K. K. (2025). Current applications and challenges in large language models for patient care: a systematic review. Communications Medicine, 5(1), 26. https://doi.org/10.1038/s43856-024-00717-2
- Callaway, E. (2025, January 23). AI-designed proteins tackle century-old problem - making snake antivenoms. Nature, 637(8047), 776. https://doi.org/10.1038/D41586-025-00133-Z
- Cao, B., Li, X., He, T., Wang, B., Zhou, S., Wu, X., & Zhang, Q. (2026). Learning Structurally Stabilized Representations for Lossless DNA Storage. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 39–47. https://doi.org/10.1609/AAAI.V40I1.36962
- Cao, B., Wang, B., & Zhang, Q. (2023). GCNSA: DNA storage encoding with a graph convolutional network and self-attention. IScience, 26(3), 106231. https://doi.org/10.1016/J.ISCI.2023.106231
- Cardiff, R. A. L., Carothers, J. M., Zalatan, J. G., & Sauro, H. M. (2024). Systems-Level Modeling for CRISPR-Based Metabolic Engineering. ACS Synthetic Biology, 13(9), 2643–2652. https://doi.org/10.1021/acssynbio.4c00053
- Carmeli, G., Paul, A. A., Kristollari, K., Eltzov, E., Batushansky, A., & Marks, R. S. (2025). Whole-Cell Fiber-Optic Biosensor for Real-Time, On-Site Sediment and Water Toxicity Assessment: Applications at Contaminated Sites Across Israel. Biosensors, 15(7), 404. https://doi.org/10.3390/BIOS15070404
- Carreras, A., Orús, R., & Casanova, D. (2026). Limitations of quantum hardware for molecular energy estimation using VQE. Physical Chemistry Chemical Physics, 28, 2834–2846. https://doi.org/10.1039/d5cp03907j
- Carruthers, D. N., Kinnunen, P. C., Li, Y., Chen, Y., Gin, J. W., Yunus, I. S., Galliard, W. R., Tan, S., Radivojevic, T., Adams, P. D., Singh, A. K., Sustarich, J., Petzold, C. J., Mukhopadhyay, A., Garcia Martin, H., & Lee, T. S. (2025). Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida. Nature Communications, 16(1), 11489. https://doi.org/10.1038/s41467-025-66304-8
- Celebi, D., Akalin, H., Yilmaz, M. T., & Dundar, M. (2023). Impacts of Biotechnologically Developed Microorganisms on Ecosystems. The EuroBiotech Journal, 7(4), 196–205. https://doi.org/10.2478/EBTJ-2023-0015
- Chen, C., & Zheng, P. (2023). Effects of down-regulation of ackA expression by CRISPR-dCpf1 on succinic acid production in Actinobacillus succinogenes. AMB Express, 13(1), 12. https://doi.org/10.1186/S13568-023-01518-X
- Chen, H., Venkatesh, M. S., Gόmez Ortega, J., Mahesh, S. V., Nandi, T. N., Madduri, R. K., Pelka, K., & Theodoris, C. V. (2026). Scaling and quantization of large-scale foundation model enables resource-efficient predictions in network biology. Nature Computational Science, 6(5), 450–463. https://doi.org/10.1038/s43588-026-00972-4
- Chen, J., Singh, N., Lu, J., Lane, S. T., & Zhao, H. (2025). Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering. Current Opinion in Biotechnology, 96, 103380. https://doi.org/10.1016/J.COPBIO.2025.103380
- Chen, Y., Hu, Z., Wu, Y., Chen, R., Jin, Y., Zhan, M., Xie, C., Chen, W., & Huang, H. (2025). Enhancing privacy in biosecurity with watermarked protein design. Bioinformatics, 41(7), btaf141. https://doi.org/10.1093/BIOINFORMATICS/BTAF141
- Cheng, A. A., & Lu, T. K. (2012). Synthetic biology: An emerging engineering discipline. Annual Review of Biomedical Engineering, 14, 155–178. https://doi.org/10.1146/annurev-bioeng-071811-150118
- Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulyte, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., Schneider, R. G., Senior, A. W., Jumper, J., Hassabis, D., Kohli, P., & Avsec, Ž. (2023). Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science, 381(6664), eadg7492. https://doi.org/10.1126/science.adg7492
- Cheng, Y., Bi, X., Xu, Y., Liu, Y., Li, J., Du, G., Lv, X., & Liu, L. (2023). Machine learning for metabolic pathway optimization: A review. Computational and Structural Biotechnology Journal, 21, 2381–2393. https://doi.org/10.1016/j.csbj.2023.03.045
- Cho, S., Shin, J., & Cho, B. K. (2018). Applications of CRISPR/Cas system to bacterial metabolic engineering. International Journal of Molecular Sciences, 19(4), 1089. https://doi.org/10.3390/ijms19041089
- Choudhury, M., Deans, A. J., Candland, D. R., & Deans, T. L. (2025). Advancing cell therapies with artificial intelligence and synthetic biology. Current Opinion in Biomedical Engineering, 34, 100580. https://doi.org/10.1016/J.COBME.2025.100580
- Daniels, K. G., Wang, S., Simic, M. S., Bhargava, H. K., Capponi, S., Tonai, Y., Yu, W., Bianco, S., & Lim, W. A. (2022). Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning. Science (New York, N.Y.), 378(6625), 1194–1200. https://doi.org/10.1126/SCIENCE.ABQ0225
- Das, P. K., Sahoo, A., & Veeranki, V. D. (2025). Modeling and Optimization of Recombinant Tocilizumab Production From Pichia pastoris Using Response Surface Methodology and Artificial Neural Network. Biotechnology and Bioengineering, 122(8), 2093–2110. https://doi.org/10.1002/BIT.29024
- Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., … Baker, D. (2022). Robust deep learning– based protein sequence design using ProteinMPNN. Science, 378(6615), 49–56. https://doi.org/10.1126/SCIENCE.ADD2187
- Ding, K., Chin, M., Zhao, Y., Huang, W., Mai, B. K., Wang, H., Liu, P., Yang, Y., & Luo, Y. (2024). Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering. Nature Communications, 15(1), 6392. https://doi.org/10.1038/s41467-024-50698-y
- DiNuzzo, M. (2022). How artificial intelligence enables modeling and simulation of biological networks to accelerate drug discovery. Frontiers in Drug Discovery, 2, 1019706. https://doi.org/10.3389/FDDSV.2022.1019706/FULL
- Donato, K., Medori, M. C., Stuppia, L., Beccari, T., Dundar, M., Marks, R. S., Michelini, S., Borghetti, E., Zuccato, C., Seppilli, L., Elsangak, H., Sozanski, G., Malacarne, D., & Bertelli, M. (2023). Unleashing the potential of biotechnology for sustainable development. European Review for Medical and Pharmacological Sciences, 27(6 Suppl), 100–113. https://doi.org/10.26355/EURREV_202312_34694
- Dudeja, C., Mishra, A., Ali, A., Singh, P. P., & Jaiswal, A. K. (2025). Microbial Genome Editing with CRISPR–Cas9: Recent Advances and Emerging Applications Across Sectors. Fermentation, 11(7), 410. https://doi.org/10.3390/FERMENTATION11070410
- Dundar, M. S., Yildirim, A. B., Yildirim, D. T., Akalin, H., & Dundar, M. (2024). Artificial cells: A potentially groundbreaking field of research and therapy. The EuroBiotech Journal, 8(1), 55–64. https://doi.org/10.2478/EBTJ-2024-0006
- Ergören, M. Ç., Senturk, N., Ali, M. S. B., Özcelik, I. Ö., Erol, K. D., Temel, S. G., & Dundar, M. (2025). ClioMD: An artificial intelligence model for ciliopathies. The EuroBiotech Journal, 9(2), 128–137. https://doi.org/10.2478/EBTJ-2025-0011
- Eskandar, K. (2026). Artificial intelligence and synthetic biology: biosecurity risks, dual-use concerns, and governance pathways. AI and Ethics, 6, 66. https://doi.org/10.1007/S43681-025-00872-9
- Fan, S. P., Okuzumi, A., Chen, P. S., Tai, Y. C., Kuo, Y. C., Tai, C. H., Li, C. H., Chiang, H. L., Hatano, T., & Lin, C. H. (2025). Parkinson’s disease in transition: Genetics, biomarkers, and emerging therapeutics. Journal of the Formosan Medical Association, S0929-6646(25), 00649–7. https://doi.org/10.1016/J.JFMA.2025.12.005
- Farea, A., Yli-Harja, O., & Emmert-Streib, F. (2025). Using Physics-Informed Neural Networks for Modeling Biological and Epidemiological Dynamical Systems. Mathematics, 13(10), 1664. https://doi.org/10.3390/MATH13101664
- Feizyab, S., Bonetti, G., Medori, M. C., Micheletti, C., Luca, I. De, Donato, K., Miertuš, J., Dundar, M. S., Vráblová, M., Henehan, G., Brown, R., Marks, R., Miertus, S., Lorusso, L., Tartaglia, G. M., Dundar, M., Michelini, S., Connelly, S. T., Bacu, A., … Bertelli, M. (2025). Artificial Intelligence and Humanoid Robotics: Bioethical Implications of Replacing Human Agency in Healthcare and beyond. The EuroBiotech Journal, 9(3), 238–246. https://doi.org/10.2478/EBTJ-2025-0019
- Ferruz, N., Schmidt, S., & Höcker, B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nature Communications, 13(1), 4348. https://doi.org/10.1038/s41467-022-32007-7
- Fukala, I., & Kučera, I. (2024). Natural Polyhydroxyalkanoates—An Overview of Bacterial Production Methods. Molecules, 29(10), 2293. https://doi.org/10.3390/MOLECULES29102293
- Galeazzi, A., Sachio, S., Edwards, E., Hilton, D., & Papathanasiou, M. M. (2025). Machine learning enhanced process design in protein a chromatography. Journal of Chromatography A, 1759, 466193. https://doi.org/10.1016/j.chroma.2025.466193
- Gangwal, A., Ansari, A., Ahmad, I., Azad, A. K., & Wan Sulaiman, W. M. A. (2024). Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review. Computers in Biology and Medicine, 179, 108734. https://doi.org/10.1016/J.COMPBIOMED.2024.108734
- Gazis, T. A., Wuyts, J., Moutsiou, A., Volpin, G., Ford, M. J., Teixeira, R. I., Wheelhouse, K. M. P., Natho, P., Žnidaršič-Plazl, P., Jost, S., Luisi, R., Benyahia, B., Maes, B. U. W., & Vilé, G. (2026). Towards greener-by-design fine chemicals. Part 1: synthetic frontiers. Chemical Society Reviews, 55(2), 619–674. https://doi.org/10.1039/D5CS00929D
- Gholap, A. D., Uddin, M. J., Faiyazuddin, M., Omri, A., Gowri, S., & Khalid, M. (2024). Advances in artificial intelligence for drug delivery and development: A comprehensive review. Computers in Biology and Medicine, 178, 108702. https://doi.org/10.1016/J.COMPBIOMED.2024.108702
- Gil, J. D., Del Rio Chanona, E. A., Guzmán, J. L., & Berenguel, M. (2026). Reinforcement learning meets bioprocess control through behavior cloning: Real-world deployment in an industrial photobioreactor. Engineering Applications of Artificial Intelligence, 164, 113326. https://doi.org/10.1016/J.ENGAPPAI.2025.113326
- Gimpel, A. L., Stark, W. J., Heckel, R., & Grass, R. N. (2023). A digital twin for DNA data storage based on comprehensive quantification of errors and biases. Nature Communications, 14(1), 6026. https://doi.org/10.1038/s41467-023-41729-1
- Gircha, A. I., Boev, A. S., Avchaciov, K., Fedichev, P. O., & Fedorov, A. K. (2023). Hybrid quantum-classical machine learning for generative chemistry and drug design. Scientific Reports, 13(1), 8250. https://doi.org/10.1038/s41598-023-32703-4
- Goktas, P., & Grzybowski, A. (2025). Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI. Journal of Clinical Medicine, 14(5), 1605. https://doi.org/10.3390/JCM14051605
- Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews. Molecular Cell Biology, 23(1), 40–55. https://doi.org/10.1038/S41580-021-00407-0
- Groff-Vindman, C. S., Trump, B. D., Cummings, C. L., Smith, M., Titus, A. J., Oye, K., Prado, V., Turmus, E., & Linkov, I. (2025). The convergence of AI and synthetic biology: the looming deluge. Npj Biomedical Innovations, 2(1), 20. https://doi.org/10.1038/s44385-025-00021-1
- Gu, Y., Su, J., Xia, J., Wu, P., Wu, H., Su, Y., Wei, P. J., & Zheng, C. H. (2025). De novo promoter design method based on deep generative and dynamic evolution algorithm. Nucleic Acids Research, 53(16), gkaf833. https://doi.org/10.1093/nar/gkaf833
- Guarda, E. C., Galinha, C. F., Pasculli, G., Duque, A. F., & Reis, M. A. M. (2025). Monitoring Polyhydroxyalkanoates (PHA) Production by Mixed Microbial Cultures Using 2D Fluorescence Spectroscopy: Impact of Operating Conditions. Natural Sciences, 5(4), e70030. https://doi.org/10.1002/ntls.70030
- Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., Badkundri, R., Shafkat, I., Gong, J., Derry, A., Molina, R. S., Thomas, N., Khan, Y. A., Mishra, C., Kim, C., … Rives, A. (2025). Simulating 500 million years of evolution with a language model. Science, 387(6736), 850–858. https://doi.org/10.1126/science.ads0018
- He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2025). Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions. IEEE Reviews in Biomedical Engineering, 18, 172–191. https://doi.org/10.1109/RBME.2024.3496744
- Hein, Z. M., Guruparan, D., Okunsai, B., Che Mohd Nassir, C. M. N., Ramli, M. D. C., & Kumar, S. (2025). AI and Machine Learning in Biology: From Genes to Proteins. Biology, 14(10), 1453. https://doi.org/10.3390/BIOLOGY14101453
- Högberg, A. (2026). Becoming human in the age of AI: cognitive co-evolutionary processes. Frontiers in Psychology, 16, 1734048. https://doi.org/10.3389/fpsyg.2025.1734048
- Hossain, I., Fanfani, V., Fischer, J., Quackenbush, J., & Burkholz, R. (2024). Biologically informed NeuralODEs for genome-wide regulatory dynamics. Genome Biology, 25(1), 127. https://doi.org/10.1186/S13059-024-03264-0
- Hussain, T., Chandio, I., Ali, A., Hyder, A., Memon, A. A., Yang, J., & Thebo, K. H. (2024). Recent developments of artificial intelligence in MXene-based devices: from synthesis to applications. Nanoscale, 16(38), 17723–17760. https://doi.org/10.1039/D4NR03050H
- IGSC. (2024). IGSC-Harmonized-Screening-Protocol-v3.0-1.
- Ing, A., Andrades, A., Cosenza, M. R., & Korbel, J. O. (2025). Integrating multimodal cancer data using deep latent variable path modelling. Nature Machine Intelligence, 7(7), 1053–1075. https://doi.org/10.1038/s42256-025-01052-4
- Iskuzhina, L., Turaev, Z., Rozhin, A., Romanov, A., Skomorokhova, E., Ishmukhametov, I., & Rozhina, E. (2025). Artificial intelligence in biology and medicine. The Science of Nature, 112(6), 80. https://doi.org/10.1007/S00114-025-02029-4
- Jeong, K. J. (2025). Advancing microbial engineering through synthetic biology. Journal of Microbiology (Seoul, Korea), 63(3), e2503100. https://doi.org/10.71150/jm.2503100
- Jeong, S. H., Lee, H. J., & Lee, S. J. (2023). Recent Advances in CRISPR-Cas Technologies for Synthetic Biology. Journal of Microbiology (Seoul, Korea), 61(1), 13. https://doi.org/10.1007/S12275-022-00005-5
- Ji, K., Yu, X., Chen, L., Wang, Y., Guo, Z., Chen, B., Li, Q., Li, Z., Zhang, H., Wang, G., Zhuang, Y., & Ruan, Y. (2025). Data-Augmented Deep Learning Algorithm for Accurate Control of Bioethanol Fermentation Using an Online Raman Analyzer. Biotechnology and Bioengineering, 122(9), 2366–2376. https://doi.org/10.1002/BIT.29040
- Ji, X., Li, Y., Wang, J., Wang, G., Ma, B., Shi, J., Cui, C., & Wang, R. (2025). Silk Protein Gene Engineering and Its Applications: Recent Advances in Biomedicine Driven by Molecular Biotechnology. Drug Design, Development and Therapy, 19, 599–626. https://doi.org/10.2147/DDDT.S504783
- Jiang, J., Li, Y., Cao, S., Shan, Y., Liu, Y., Fei, T., Yu, Y., Feng, Y., Li, Y., Li, Y., & Yuan, J. (2025). Artificial intelligence in bioinformatics: a survey. Briefings in Bioinformatics, 26(6), bbaf576. https://doi.org/10.1093/BIB/BBAF576
- Jin, S., Wu, Q., Fu, G., Lu, D., Wang, F., Deng, L., & Nie, K. (2025). Breaking Evolution’s Ceiling: AI-Powered Protein Engineering. Catalysts, 15(9), 842. https://doi.org/10.3390/catal15090842
- Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
- Kacena, M. A., Plotkin, L. I., & Fehrenbacher, J. C. (2024). The Use of Artificial Intelligence in Writing Scientific Review Articles. Current Osteoporosis Reports, 22(1), 115–121. https://doi.org/10.1007/S11914-023-00852-0
- Kalia, V. C., Singh, R. V., Gong, C., & Lee, J. K. (2025). Toward Sustainable Polyhydroxyalkanoates: A Next-Gen Biotechnology Approach. Polymers, 17(7), 853. https://doi.org/10.3390/POLYM17070853
- Karimi Alavijeh, M., Lee, Y. Y., & Gras, S. L. (2024). A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments. Engineering in Life Sciences, 24(7), e2400023. https://doi.org/10.1002/ELSC.202400023
- Kashiwaya, S., Shi, Y., Lu, J., Sangiovanni, D. G., Greczynski, G., Magnuson, M., Andersson, M., Rosen, J., & Hultman, L. (2024). Synthesis of goldene comprising single-atom layer gold. Nature Synthesis, 3(6), 744–751. https://doi.org/10.1038/s44160-024-00518-4
- Khalil, A. S., & Collins, J. J. (2010). Synthetic biology: applications come of age. Nature Reviews Genetics, 11(5), 367–379. https://doi.org/10.1038/nrg2775
- Khan, M. F., & Khan, M. T. (2025). AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications. Molecules, 31(1), 45. https://doi.org/10.3390/MOLECULES31010045
- Kiani, A. K., Pheby, D., Henehan, G., Brown, R., Sieving, P., Sykora, P., Marks, R., Falsini, B., Capodicasa, N., Miertus, S., Lorusso, L., Dondossola, D., Tartaglia, G. M., Ergoren, M. C., Dundar, M., Michelini, S., Malacarne, D., Bonetti, G., Dautaj, A., … Bertelli, M. (2022). Ethical considerations regarding animal experimentation. Journal of Preventive Medicine and Hygiene, 63(2S3), E255–E255. https://doi.org/10.15167/2421-4248/JPMH2022.63.2S3.2768
- Kim, G. B., Choi, S. Y., Cho, I. J., Ahn, D. H., & Lee, S. Y. (2023). Metabolic engineering for sustainability and health. Trends in Biotechnology, 41(3), 425–451. https://doi.org/10.1016/j.tibtech.2022.12.014
- Kim, M. G., Go, M. J., Kang, S. H., Jeong, S. H., & Lim, K. (2025). Revolutionizing CRISPR technology with artificial intelligence. Experimental & Molecular Medicine, 57(7), 1419–1431. https://doi.org/10.1038/s12276-025-01462-9
- Korbeld, K. T., Viliuga, V., & Fürst, M. J. L. J. (2026). Limitations of the refolding pipeline for de novo protein design. Protein Science, 35(6), e70613. https://doi.org/10.1002/PRO.70613
- Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., McHugh, R., Vafeados, D., Li, X., Sutherland, G. A., Hitchcock, A., Neil Hunter, C., Kang, A., Brackenbrough, E., Bera, A. K., … Baker, D. (2024). Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science, 384(6693), eadl2528. https://doi.org/10.1126/science.adl2528
- Kuchana, S. K., Repalle, U. K., Alahari, N. V., Kondamuri, M., Manduva, S. K., Vanguru, R. V., Gorle, S. A., & Alahari, S. K. (2026). Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies. Cancers, 18(10), 1543. https://doi.org/10.3390/CANCERS18101543
- Landwehr, G. M., Bogart, J. W., Magalhaes, C., Hammarlund, E. G., Karim, A. S., & Jewett, M. C. (2025). Accelerated enzyme engineering by machine-learning guided cell-free expression. Nature Communications, 16(1), 865. https://doi.org/10.1038/s41467-024-55399-0
- Lappala, A. (2024). The next revolution in computational simulations: Harnessing AI and quantum computing in molecular dynamics. Current Opinion in Structural Biology, 89, 102919. https://doi.org/10.1016/j.sbi.2024.102919
- Lawson, C. E., Martí, J. M., Radivojevic, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., Singer, S. W., Mukhopadhyay, A., Tanjore, D., Dunn, J. G., & Garcia Martin, H. (2021). Machine learning for metabolic engineering: A review. Metabolic Engineering, 63, 34–60. https://doi.org/10.1016/j.ymben.2020.10.005
- Lee, J. W., Na, D., Park, J. M., Lee, J., Choi, S., & Lee, S. Y. (2012). Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nature Chemical Biology, 8(6), 536–546. https://doi.org/10.1038/nchembio.970
- Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., Kaissis, G., Tsakou, G., Buvat, I., Kalpathy-Cramer, J., Mongan, J., Schnabel, J. A., Kushibar, K., Riklund, K., Marias, K., … Starmans, M. P. A. (2025). FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ, 388, e081554. https://doi.org/10.1136/BMJ-2024-081554
- Lemperle, M., Ramin, P., Kager, J., Cassells, B., Stocks, S., & Gernaey, K. V. (2026). Hybrid modeling for industrial fermentation processes with an “Intra-Batch Experimental Design.” Journal of Industrial Microbiology & Biotechnology, 53, kuag014. https://doi.org/10.1093/JIMB/KUAG014
- Li, H., Wu, Y., Qin, D., Xie, J., Huang, W., Chen, R., Liu, H., Wang, Y., & Zhao, D. (2025). Phase-driven rewiring in Escherichia coli enhances coenzyme Q10 biosynthesis via temporal and energetic coordination. Applied Microbiology and Biotechnology, 109(1), 248. https://doi.org/10.1007/S00253-025-13619-7
- Li, W., Mao, Z., Xiao, Z., Liao, X., Koffas, M., Chen, Y., Ma, H., & Tang, Y. J. (2025). Large language model for knowledge synthesis and AI-enhanced biomanufacturing. Trends in Biotechnology, 43(8), 1864–1875. https://doi.org/10.1016/J.TIBTECH.2025.02.008
- Liebal, U. W., Köbbing, S., Netze, L., Schweidtmann, A. M., Mitsos, A., & Blank, L. M. (2021). Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb. Frontiers in Bioinformatics, 1, 747428. https://doi.org/10.3389/fbinf.2021.747428
- Ligarda-Samanez, C. A., Huamán-Carrión, M. L., Palomino-Rincón, H., Taipe-Pardo, F., Moscoso-Moscoso, E., Cabel-Moscoso, D. J., Garcia-Espinoza, A. J., Calderón Huamaní, D. F., Romero Plasencia, J. M., Martinez-Hernandez, J. A., Luciano-Alipio, R., & Apaza-Cruz, J. (2026). Sustainable Biopolymers for Environmental Applications: Advances and Future Perspectives Toward a Circular Economy. Polymers, 18(5), 618. https://doi.org/10.3390/POLYM18050618
- Lingė, D., Gedgaudas, M., Merkys, A., Petrauskas, V., Vaitkus, A., Grybauskas, A., Paketurytė, V., Zubrienė, A., Zakšauskas, A., Mickevičiūtė, A., Smirnovienė, J., Baranauskienė, L., Crossed D sign apkauskaitė, E., Dudutienė, V., Urniežius, E., Konovalovas, A., Kazlauskas, E., Shubin, K., Schiöth, H. B., … Matulis, D. (2023). PLBD: protein–ligand binding database of thermodynamic and kinetic intrinsic parameters. Database, 2023, baad040. https://doi.org/10.1093/DATABASE/BAAD040
- Listgarten, J., & Jiang, H. (2026). How artificial intelligence is reengineering protein engineering. Science, 392(6794), 159–166. https://doi.org/10.1126/SCIENCE.AEC8444
- Liu, Y., Wang, S., Dong, J., Chen, L., Wang, X., Wang, L., Li, F., Wang, C., Zhang, J., Wang, Y., Wei, S., Chen, Q., & Liu, H. (2024). De novo protein design with a denoising diffusion network independent of pretrained structure prediction models. Nature Methods, 21(11), 2107–2116. https://doi.org/10.1038/s41592-024-02437-w
- Ljubešić, Z., Gligora Udovič, M., Overlingė, D., Grgurević, F., Akgül, F., Bacu, A., Díaz-Marrero, A. R., Drakulović, D., Fazi, S., Gaudêncio, S. P., Kolda, A., Novoveska, L., Safarik, I., Sousa, J. R., Thomas, O. P., Reddy, M. M., Varese, G. C., Vasquez, M. I., Makovec, T., & Rotter, A. (2025). Exploring marine microbial diversity: an overview of representative sampling strategies. Frontiers in Marine Science, 12, 1597865. https://doi.org/10.3389/fmars.2025.1597865
- Lu, Q., Zhang, H., Fan, R., Wan, Y., & Luo, J. (2025). Machine learning-based Bayesian optimization facilitates ultrafiltration process design for efficient protein purification. Separation and Purification Technology, 363, 132122. https://doi.org/10.1016/j.seppur.2025.132122
- Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., Fraser, J. S., & Naik, N. (2023). Large language models generate functional protein sequences across diverse families. Nature Biotechnology, 41(8), 1099–1106. https://doi.org/10.1038/s41587-022-01618-2
- Maizels, R. J., & Briscoe, J. (2026). Gene regulatory networks: from correlative models to causal explanations. Nature Reviews. Genetics. https://doi.org/10.1038/S41576-026-00939-1
- Malpetti, D., Scutari, M., Gualdi, F., van Setten, J., van der Laan, S., Haitjema, S., Lee, A. M., Hering, I., & Mangili, F. (2025). Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities. Frontiers in Digital Health, 7, 1644291. https://doi.org/10.3389/FDGTH.2025.1644291/TEXT
- Marchetti, L., Nifosì, R., Martelli, P. L., Da Pozzo, E., Cappello, V., Banterle, F., Trincavelli, M. L., Martini, C., & D’Elia, M. (2022). Quantum computing algorithms: getting closer to critical problems in computational biology. Briefings in Bioinformatics, 23(6), bbac437. https://doi.org/10.1093/bib/bbac437
- Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., Silva, I., Magalhães, P., Schmidt, S., & Vale, N. (2024). Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare. Pharmaceutics, 16(3), 332. https://doi.org/10.3390/PHARMACEUTICS16030332
- Martin, D. K., Vicente, O., Beccari, T., Kellermayer, M., Koller, M., Lal, R., Marks, R. S., Marova, I., Mechler, A., Tapaloaga, D., Žnidaršič-Plazl, P., & Dundar, M. (2021). A brief overview of global biotechnology. Biotechnology & Biotechnological Equipment, 35(1), 354–363. https://doi.org/10.1080/13102818.2021.1878933
- Matsubara, T., Machida, S., Owusu, S. P. K., Asakura, A., Hashimoto, H., Matsuoka, M., & Nagasaki, M. (2025). QTFPred: robust high-performance quantum machine learning modeling that predicts main and cooperative transcription factor bindings with base resolution. Briefings in Bioinformatics, 26(6), bbaf604. https://doi.org/10.1093/bib/bbaf604
- Maurizio, A., & Mazzola, G. (2025). Quantum Computing for Genomics: Conceptual Challenges and Practical Perspectives. PRX Life, 3(4), 047001. https://doi.org/10.1103/h49j-bsc6
- McKenzie, M., Irac, S. E., Chen, Z., Moradi, A., Jenner, A., Nguyen, Q., & Rashidieh, B. (2026). Integrative spatial omics and artificial intelligence: transforming cancer research with omics data and AI. Seminars in Cancer Biology, 119, 65–82. https://doi.org/10.1016/j.semcancer.2026.01.002
- Medori, M. C., Bonetti, G., Donato, K., Dhuli, K., Henehan, G., Brown, R., Sieving, P., Sykora, P., Marks, R., Falsini, B., Capodicasa, N., Miertus, S., Lorusso, L., Dondossola, D., Tartaglia, G. M., Tartaglia, G. M., Ergoren, M. C., Dundar, M., Michelini, S., … Bertelli, M. (2023). Bioetics Issues of Artificial Placenta and Artificial Womb Technology. La Clinica Terapeutica, 174(Suppl 2(6)), 243–248. https://doi.org/10.7417/CT.2023.2494
- Meiser, L. C., Nguyen, B. H., Chen, Y. J., Nivala, J., Strauss, K., Ceze, L., & Grass, R. N. (2022). Synthetic DNA applications in information technology. Nature Communications, 13(1), 352. https://doi.org/10.1038/s41467-021-27846-9
- Mirchandani, I., Khandhediya, Y., & Chauhan, K. (2025). Review on Advancement of AI in Synthetic Biology. Methods in Molecular Biology, 2952, 483–490. https://doi.org/10.1007/978-1-0716-4690-8_26
- Nam, Y., Kim, J., Jung, S. H., Woerner, J., Suh, E. H., Lee, D. G., Shivakumar, M., Lee, M. E., & Kim, D. (2024). Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. Annual Review of Biomedical Data Science, 7(1), 225–250. https://doi.org/10.1146/annurevbiodatasci-102523-103801
- Narayanan, H., & Love, J. C. (2026). Pichia-CLM: A language model-based codon optimization pipeline for Komagataella phaffii. Proceedings of the National Academy of Sciences of the United States of America, 123(8), e2522052123. https://doi.org/10.1073/PNAS.2522052123
- Nassir, N., Hashmi, M. A., Raji, K. G., Jamalalail, B., Maksymowsky, A., Scherer, S. W., Alsheikh-Ali, A., & Uddin, M. (2025). Quantum computing and the implementation of precision medicine. Npj Genomic Medicine, 10(1), 80. https://doi.org/10.1038/s41525-025-00537-w
- Nguyen, E., Poli, M., Durrant, M. G., Kang, B., Katrekar, D., Li, D. B., Bartie, L. J., Thomas, A. W., King, S. H., Brixi, G., Sullivan, J., Ng, M. Y., Lewis, A., Lou, A., Ermon, S., Baccus, S. A., Hernandez-Boussard, T., Ré, C., Hsu, P. D., & Hie, B. L. (2024). Sequence modeling and design from molecular to genome scale with Evo. Science, 386(6723). https://doi.org/10.1126/science.ado9336
- Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D., & Voigt, C. A. (2016). Genetic circuit design automation. Science, 352(6281). https://doi.org/10.1126/science.aac7341
- Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023). ProGen2: Exploring the boundaries of protein language models. Cell Systems, 14(11), 968-978. e3. https://doi.org/10.1016/j.cels.2023.10.002
- Nilsson, A., Meimetis, N., & Lauffenburger, D. A. (2025). Towards an interpretable deep learning model of cancer. Npj Precision Oncology, 9(1), 46. https://doi.org/10.1038/s41698-025-00822-y
- Notin, P., Rollins, N., Gal, Y., Sander, C., & Marks, D. (2024). Machine learning for functional protein design. Nature Biotechnology, 42(2), 216–228. https://doi.org/10.1038/s41587-024-02127-0
- O’Connor, S., Yan, Y., Thilo, F. J. S., Felzmann, H., Dowding, D., & Lee, J. J. (2023). Artificial intelligence in nursing and midwifery: A systematic review. Journal of Clinical Nursing, 32(13–14), 2951–2968. https://doi.org/10.1111/JOCN.16478
- OECD. (2025). SYNTHETIC BIOLOGY, AI AND AUTOMATION: A FORWARD-LOOKING TECHNOLOGY ASSESSMENT. https://www.oecd.org/en/publications/synthetic-biology-ai-and-automation_12158721-en.html
- Oetomo, B., Luo, L., Qu, Y., Discepola, M., Kentish, S. E., & Gras, S. L. (2025). Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review. Digital Chemical Engineering, 14, 100211. https://doi.org/10.1016/J.DCHE.2024.100211
- Ozcelik, F., Dundar, M. S., Yildirim, A. B., Henehan, G., Vicente, O., Sánchez-Alcázar, J. A., Gokce, N., Yildirim, D. T., Bingol, N. N., Karanfilska, D. P., Bertelli, M., Pojskic, L., Ercan, M., Kellermayer, M., Sahin, I. O., Greiner-Tollersrud, O. K., Tan, B., Martin, D., Marks, R., … Dundar, M. (2024). The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution. Functional & Integrative Genomics, 24(4), 138. https://doi.org/10.1007/S10142-024-01417-9
- Pal, S., Bhattacharya, M., Lee, S. S., & Chakraborty, C. (2024). Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics. Molecular Biotechnology, 66(2), 163–178. https://doi.org/10.1007/s12033-023-00765-4
- Palit, P., Minkara, M., Abida, M., Marwa, S., Sen, C., Roy, A., Pasha, M. R., Mosae, P. S., Saha, A., & Ferdoush, J. (2025). PlastiCRISPR: Genome Editing-Based Plastic Waste Management with Implications in Polyethylene Terephthalate (PET) Degradation. Biomolecules, 15(5), 684. https://doi.org/10.3390/BIOM15050684
- Pamidimukkala, J. V., Bopardikar, S., Dakshinamoorthy, A., Kannan, A., Dasgupta, K., & Senapati, S. (2024). Protein Structure Prediction with High Degrees of Freedom in a Gate-Based Quantum Computer. Journal of Chemical Theory and Computation, 20(22), 10223–10234. https://doi.org/10.1021/acs.jctc.4c00848
- Pannu, J., Bloomfield, D., MacKnight, R., Hanke, M. S., Zhu, A., Gomes, G., Cicero, A., & Inglesby, T. V. (2025). Dual-use capabilities of concern of biological AI models. PLoS Computational Biology, 21(5), e1012975. https://doi.org/10.1371/journal.pcbi.1012975
- Peykani, P., Ramezanlou, F., Tanasescu, C., & Ghanidel, S. (2025). Large Language Models: A Structured Taxonomy and Review of Challenges, Limitations, Solutions, and Future Directions. Applied Sciences, 15(14), 8103. https://doi.org/10.3390/app15148103
- Pinto, J., Ramos, J. R. C., Costa, R. S., & Oliveira, R. (2023). A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard. AI (Switzerland), 4(1), 303–318. https://doi.org/10.3390/ai4010014
- Plante, M., Champie, A., Michaud, F., & Rodrigue, S. (2026). Toward full automation in synthetic biology: A progressive conceptual framework integrating robotics and intelligent agents. SLAS Technology, 36, 100378. https://doi.org/10.1016/J.SLAST.2025.100378
- Priyadharshini, M., Raju, B. D., Banu, A. F., Kumar, P. J., Murugesh, V., & Rybin, O. (2025). A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data. Scientific Reports, 15(1), 26553. https://doi.org/10.1038/s41598-025-06544-2
- Puniya, B. L. (2025). Artificial-intelligence-driven Innovations in Mechanistic Computational Modeling and Digital Twins for Biomedical Applications. Journal of Molecular Biology, 437(17), 169181. https://doi.org/10.1016/J.JMB.2025.169181
- Qin, K., Liu, F., Zhang, C., Deng, R., Fernie, A. R., & Zhang, Y. (2025). Systems and synthetic biology for plant natural product pathway elucidation. Cell Reports, 44(6), 115715. https://doi.org/10.1016/j.celrep.2025.115715
- Quek, Y., Stilck França, D., Khatri, S., Meyer, J. J., & Eisert, J. (2024). Exponentially tighter bounds on limitations of quantum error mitigation. Nature Physics, 20(10), 1648–1658. https://doi.org/10.1038/s41567-024-02536-7
- Radivojević, T., Costello, Z., Workman, K., & Garcia Martin, H. (2020). A machine learning Automated Recommendation Tool for synthetic biology. Nature Communications, 11(1), 4879. https://doi.org/10.1038/s41467-020-18008-4
- Rai, K., Wang, Y., O’Connell, R. W., Patel, A. B., & Bashor, C. J. (2024). Using machine learning to enhance and accelerate synthetic biology. Current Opinion in Biomedical Engineering, 31, 100553. https://doi.org/10.1016/J.COBME.2024.100553
- Rashidi, M., Arima, S., Stetco, A. C., Coppola, C., Musarò, D., Greco, M., Damato, M., My, F., Lupo, A., Lorenzo, M., Danieli, A., Maruccio, G., Argentiero, A., Buccoliero, A., Donzella, M. D., & Maffia, M. (2025). Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning. Brain Sciences, 15(7), 739. https://doi.org/10.3390/BRAINSCI15070739
- Richter, J., Wang, Q., Lange, F., Thiel, P., Yilmaz, N., Solle, D., Zhuang, X., & Beutel, S. (2025). Machine Learning-Powered Optimization of a CHO Cell Cultivation Process. Biotechnology and Bioengineering, 122(5), 1153–1164. https://doi.org/10.1002/BIT.28943
- Rotter, A., Bacu, A., Barbier, M., Bertoni, F., Bones, A. M., Cancela, M. L., Carlsson, J., Carvalho, M. F., Cegłowska, M., Dalay, M. C., Dailianis, T., Deniz, I., Drakulovic, D., Dubnika, A., Einarsson, H., Erdoğan, A., Eroldoğan, O. T., Ezra, D., Fazi, S., … Vasquez, M. I. (2020). A New Network for the Advancement of Marine Biotechnology in Europe and Beyond. Frontiers in Marine Science, 7, 524577. https://doi.org/10.3389/FMARS.2020.00278/TEXT
- Selvaraj, A. A., Jayawant, M., Kayalvizhi, N., Krishnan, M., Puvaneshvari, N., Santhosh Kumar, A. W., & Rameshkumar, N. (2026). Exploring quantum frontiers in protein structure prediction: techniques, challenges, and opportunities. Methods, 251, 94–112. https://doi.org/10.1016/J.YMETH.2026.04.006
- Serag, I., Azzam, A. Y., Hassan, A. K., Diab, R. A., Diab, M., Hefnawy, M. T., Ali, M. A., & Negida, A. (2025). Multimodal diagnostic tools and advanced data models for detection of prodromal Parkinson’s disease: a scoping review. BMC Medical Imaging, 25(1), 103. https://doi.org/10.1186/S12880-025-01620-5/TABLES/1
- Shi, J., Bendig, D., Vollmar, H. C., & Rasche, P. (2023). Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. Journal of Medical Internet Research, 25(1), e45815. https://doi.org/10.2196/45815
- Shokrpour, S., MoghadamFarid, A. M., Bazzaz Abkenar, S., Haghi Kashani, M., Akbari, M., & Sarvizadeh, M. (2025). Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges. Npj Parkinson’s Disease, 11(1), 187. https://doi.org/10.1038/s41531-025-01025-9
- Shrivastava, A., Nikita, S., & Rathore, A. S. (2024). Machine learning tool as an enabler for rapid quantification of monoclonal antibodies N-glycans using fluorescence detector. International Journal of Biological Macromolecules, 271, 132694. https://doi.org/10.1016/J.IJBIOMAC.2024.132694
- Singh, N., Lane, S., Yu, T., Lu, J., Ramos, A., Cui, H., & Zhao, H. (2025). A generalized platform for artificial intelligence-powered autonomous enzyme engineering. Nature Communications, 16(1), 5648. https://doi.org/10.1038/s41467-025-61209-y
- Sokra, I., & Meta, H. (2026). Bioplastics in the Circular Bioeconomy: Production Pathways, Biodegradation Mechanisms, and Environmental Implications. Journal of Agriculture and Technology, 2(2), 23–35. https://doi.org/10.6084/m9.figshare.30986584
- Sola, P. V., Montenegro, J. F., Moreira, S. I., Lorenzo, F. R., Vandervorst, P., González, E. P., Lorenzo, M. P., Couñago, I. P., Landín, S. M., Castilla, L. H., Quinteiro, J. I., Álvarez Rodríguez, J. A., Algarra, B. A., Flo, E. G., & García, J. (2025). PHB in cyanobacteria: analyzing production through images processing and FT-IR techniques. New Biotechnology, 89, 119–129. https://doi.org/10.1016/j.nbt.2025.07.003
- Srivastava, S., Benerjee, K. G., & Banerjee, A. (2024). Efficient Bidirectional RNNs for Substitution Error Correction in DNA Data Storage. 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), 434–439. https://doi.org/10.1109/ICMLCN59089.2024.10625179
- Sung, J. Y., Park, S. min, & Cheong, J. H. (2026). Quantum computing for biotechnological innovation: transformative potential in biomedicine and drug discovery. Trends in Biotechnology, S0167-7799(26), 00008–00009. https://doi.org/10.1016/J.TIBTECH.2026.01.008
- Tankhilevich, E., Martinez Cuesta, S., Barrett, I., Berg, C., Holmberg Schiavone, L., & Leach, A. R. (2026). RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli. Bioinformatics, 42(1), btag003. https://doi.org/10.1093/BIOINFORMATICS/BTAG003
- Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., & Ellinor, P. T. (2023). Transfer learning enables predictions in network biology. Nature 2023 618:7965, 618(7965), 616–624. https://doi.org/10.1038/s41586-023-06139-9
- Thomson, T., Li, G., Strilchuk, A., Cui, H., Wang, B., & Li, B. (2025). Harnessing artificial intelligence to advance CRISPR-based genome editing technologies. Nature Reviews Genetics, 27(3), 212–230. https://doi.org/10.1038/s41576-025-00907-1
- Tobias, A. V., & Wahab, A. (2025). Autonomous ‘self-driving’ laboratories: a review of technology and policy implications. Royal Society Open Science, 12(7), 250646. https://doi.org/10.1098/RSOS.250646/235354
- Trif, C., Le, S. Q., Vunduk, J., Parcharoen, Y., & Marks, R. S. (2025). A novel bioluminescent bacteria-based method coupled with dynamic time warping for detecting and differentiating copper and mercury in water. Water Research, 286, 124230. https://doi.org/10.1016/J.WATRES.2025.124230
- Trif, C., Vunduk, J., Parcharoen, Y., Bualuang, A., & Marks, R. S. (2024). Bioluminescent Whole-Cell Bioreporter Bacterial Panel for Sustainable Screening and Discovery of Bioactive Compounds Derived from Mushrooms. Biosensors, 14(11), 558. https://doi.org/10.3390/BIOS14110558
- Tudor, B. H., Shargo, R., Gray, G. M., Fierstein, J. L., Kuo, F. H., Burton, R., Johnson, J. T., Scully, B. B., Asante-Korang, A., Rehman, M. A., & Ahumada, L. M. (2025). A scoping review of human digital twins in healthcare applications and usage patterns. Npj Digital Medicine, 8(1), 587. https://doi.org/10.1038/s41746-025-01910-w
- Urbina, F., Lentzos, F., Invernizzi, C., & Ekins, S. (2022). Dual use of artificial-intelligence-powered drug discovery. Nature Machine Intelligence, 4(3), 189–191. https://doi.org/10.1038/s42256-022-00465-9
- Uttarkar, A., & Niranjan, V. (2024). Quantum synergy in peptide folding: A comparative study of CVaR-variational quantum eigensolver and molecular dynamics simulation. International Journal of Biological Macromolecules, 273, 133033. https://doi.org/10.1016/J.IJBIOMAC.2024.133033
- Uttarkar, A., Niranjan, V., Saxena, A., & Kumar, V. (2026). QuPepFold: A python package for hybrid quantum-classical protein folding simulations with CVaR-optimized VQE. PloS One, 21(2), e0342012. https://doi.org/10.1371/JOURNAL.PONE.0342012
- Valencia-Velásquez, J., Yaker-Moreno, H. A., Martínez-Guerrero, A., Ibáñez-Espinel, F., Pérez-Correa, J. R., & Caicedo-Ortega, N. H. (2025). Advancing hybrid modeling of Saccharomyces cerevisiae fermentation with mixed carbon sources and urea in a mini-stirred tank reactor. Bioprocess and Biosystems Engineering, 48(11), 1919–1937. https://doi.org/10.1007/S00449-025-03222-5
- Vasina, M., Kovar, D., Damborsky, J., Ding, Y., Yang, T., deMello, A., Mazurenko, S., Stavrakis, S., & Prokop, Z. (2023). In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning. Biotechnology Advances, 66, 108171. https://doi.org/10.1016/J.BIOTECHADV.2023.108171
- Vázquez Torres, S., Benard Valle, M., Mackessy, S. P., Menzies, S. K., Casewell, N. R., Ahmadi, S., Burlet, N. J., Muratspahić, E., Sappington, I., Overath, M. D., Riverade-Torre, E., Ledergerber, J., Laustsen, A. H., Boddum, K., Bera, A. K., Kang, A., Brackenbrough, E., Cardoso, I. A., Crittenden, E. P., … Baker, D. (2025). De novo designed proteins neutralize lethal snake venom toxins. Nature, 639(8053), 225–231. https://doi.org/10.1038/s41586-024-08393-x
- Volk, M. J., Lourentzou, I., Mishra, S., Vo, L. T., Zhai, C., & Zhao, H. (2020). Biosystems Design by Machine Learning. ACS Synthetic Biology, 9(7), 1514–1533. https://doi.org/10.1021/ACSSYNBIO.0C00129
- Wahab, W. A. A. (2025). Review of research progress in immobilization and chemical modification of microbial enzymes and their application. Microbial Cell Factories, 24(1), 167. https://doi.org/10.1186/S12934-025-02791-0
- Wang, X., Xu, K., Huang, Z., Lin, Y., Zhou, J., Zhou, L., & Ma, F. (2025). Accelerating promoter identification and design by deep learning. Trends in Biotechnology, 43(12), 3071–3087. https://doi.org/10.1016/j.tibtech.2025.05.008
- Wang, Y., Wang, H., Wei, L., Li, S., Liu, L., & Wang, X. (2020). Synthetic promoter design in Escherichia coli based on a deep generative network. Nucleic Acids Research, 48(12), 6403–6412. https://doi.org/10.1093/nar/gkaa325
- Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Hanikel, N., Pellock, S. J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S. V., … Baker, D. (2023). De novo design of protein structure and function with RFdiffusion. Nature, 620(7976), 1089–1100. https://doi.org/10.1038/s41586-023-06415-8
- Wen, G., & Li, L. (2025). Federated transfer learning with differential privacy for multi-omics survival analysis. Briefings in Bioinformatics, 26(2), bbaf166. https://doi.org/10.1093/BIB/BBAF166
- Wittmann, B. J., Alexanian, T., Bartling, C., Beal, J., Clore, A., Diggans, J., Flyangolts, K., Gemler, B. T., Mitchell, T., Murphy, S. T., Wheeler, N. E., & Horvitz, E. (2025). Strengthening nucleic acid biosecurity screening against generative protein design tools. Science, 390(6768), 82–87. https://doi.org/10.1126/science.adu8578
- Wu, W., Xiang, L., Liu, Q., & Yang, K. (2023). Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization. IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, 9(4), 461–471. https://doi.org/10.1109/TMBMC.2023.3331579
- Xia, Y., & Huo, Y. X. (2026). Controlling gene expression using AI designed Cis-regulatory elements. Biotechnology Advances, 87, 108802. https://doi.org/10.1016/j.biotechadv.2026.108802
- Xu, D., Liu, B., Wang, J., & Zhang, Z. (2022). Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers. Frontiers in Bioengineering and Biotechnology, 10, 998298. https://doi.org/10.3389/FBIOE.2022.998298
- Xu, K., Yu, S., Wang, K., Tan, Y., Zhao, X., Liu, S., Zhou, J., & Wang, X. (2024). AI and Knowledge-Based Method for Rational Design of Escherichia coli Sigma70 Promoters. ACS Synthetic Biology, 13(1), 402–407. https://doi.org/10.1021/acssynbio.3c00578
- Yadav, J., Marwah, H., & Kumar, C. (2025). Synthetic biology and metabolic engineering paving the way for sustainable next-gen biofuels: a comprehensive review. Energy Advances, 4(10), 1209–1228. https://doi.org/10.1039/D5YA00118H
- Yang, T., Xiao, Y., Bao, Z., Hao, J., & Peng, J. (2025). The rise and potential opportunities of large language model agents in bioinformatics and biomedicine. Briefings in Bioinformatics, 26(6), bbaf601. https://doi.org/10.1093/BIB/BBAF601
- Yetgin, A. (2025). Revolutionizing multi-omics analysis with artificial intelligence and data processing. Quantitative Biology, 13(3), e70002. https://doi.org/10.1002/qub2.70002
- Yildirim, D. T., Yildirim, A. B., Martin, D., Beccari, T., Vicente, O., Radecka, I., Tchuenbou-Magaia, F., Marks, R., Lal, R., Prakash, S., Mechler, A., Milkov, M. P., Georgieva, S. F., Iliev, I., Kaneva, R., Gokce, N., Yaneva, G. A., Agova, N. V., Ivanova, N. D., … Dundar, M. (2026). Integrated Biotechnological Strategies for Planetary Health and Ecosystem Restoration. The EuroBiotech Journal, 10(2), 81–102. https://doi.org/10.2478/EBTJ-2026-0009
- Yin, S. (2025). Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects. Biosensors, 15(10), 656. https://doi.org/10.3390/BIOS15100656
- Young, A. L., Oxtoby, N. P., Garbarino, S., Fox, N. C., Barkhof, F., Schott, J. M., & Alexander, D. C. (2024). Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nature Reviews Neuroscience, 25(2), 111–130. https://doi.org/10.1038/s41583-023-00779-6
- Yuan, S., Xu, V., Muddana, C., Sureshkumar, P., & Tang, Y. J. (2026). From design–build–test–learn cycles to AI-driven digital twins for bioprocess scale-up in the Genesis Mission era. Current Opinion in Biotechnology, 100, 103516. https://doi.org/10.1016/J.COPBIO.2026.103516
- Zack, M., Stupichev, D. N., Moore, A. J., Slobodchikov, I. D., Sokolov, D. G., Trifonov, I. F., & Gobbs, A. (2025). Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine. Mayo Clinic Proceedings: Digital Health, 3(3), 100246. https://doi.org/10.1016/j.mcpdig.2025.100246
- Zampieri, G., Sandner, V., Verma, S., Kraemer, J., Lennon, C., Occhipinti, A., McCreath, G., & Angione, C. (2026). Bioprocess optimisation via joint machine learning and metabolic modelling. Metabolic Engineering, 96, 113–128. https://doi.org/10.1016/j.ymben.2026.03.004
- Zhang, G., Liu, C., Lu, J., Zhang, S., & Zhu, L. (2025). The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe. Biology, 14(9), 1268. https://doi.org/10.3390/biology14091268
- Zhou, C., Jiang, F., Chen, W., Nugen, S. R., & Huang, C. (2025). Synthetic biology meets diagnostics: Engineering biosensing platforms for rapid and accurate pathogen and viral detection. Biosensors and Bioelectronics, 290, 117946. https://doi.org/10.1016/J.BIOS.2025.117946
- Zhou, X., Ding, N., Zhou, S., & Deng, Y. (2026). AI-Guided Design and Predictive Modeling of Synthetic Escherichia coli Promoters through Comprehensive −10/–35 Box Engineering. ACS Synthetic Biology, 15(2), 728–739. https://doi.org/10.1021/ACSSYNBIO.5C00765
- Zubrienė, A., Kurtenoka, M., Paketurytė-Latvė, V., Leitans, J., Manakova, E., Žvirblis, M., Kazaks, A., Eimonta, V., Tars, K., Gražulis, S., Petrauskas, V., Matulienė, J., Dudutienė, V., Shubin, K., & Matulis, D. (2026). Achieving femtomolar affinities in structure-based drug design. European Biophysics Journal, 55(1), 55–62. https://doi.org/10.1007/S00249-025-01812-5
© 2026 Izem Olcay Sahin, Nuriye Gokce, Duygu T. Yildirim, A. Baki Yildirim, Hilal Akalın, Donald Martin, Tommaso Beccari, Oscar Vicente, Iza Radecka, Fideline Tchuenbou-Magaia, Robert S. Marks, Ratnesh Lal, Satya Prakash, Adam Mechler, Mario Petrov Milkov, Svetlana Fotkova Georgieva, Ilia Iliev, Kosi Gramatikoff, Ed Judge, Milica Markovic, Radka Kaneva, Galina Aleksieva Yaneva, Nadya Vasileva Agova, Nikoleta Dobromirova Ivanova, Mariya Kiryakova Tsvetkova, Ivelin Rosenov Iliev, Michel Salzet, Kisung Ko, Michele Maffia, Chiara Coppola, Matteo Bertelli, Isabelle Fournier, Lejla Pojskic, Qun Sun, Lembit Nei, Reynir Armgrisson, Gary Henehan, Daumantas Matulis, Dijana Plaseska-Karanfilska, K. Santacruz-Gomez, Isabel Belo, Štefánia Hrončeková, Sehime G. Temel, Ole K. Greiner-Tollersrud, Dana Tapaloaga, Andreas Janecke, Ivana Marova, Benedetta Spedicato, Polona Žnidaršič-Plazl, Igor Plazl, Ariola Bacu, Anargyros N. Moulas, Alexander Kilchevsky, M. Sait Dundar, P. Bartolini, Anita Slavica, Francisco Fuentes, Jose Carlos Lorenzo Feijoo, Andrés Izquierdo Romero, Helal Ragab Moussa, Amin Hejazi, Attya Bhatti, Bajram Berisha, Alma Kokhmetova, Nikolai Zhelev, Juraj Krajcovic, Viktor Nedovic, Alla Salmina, Mark Nujiten, İrem Kalay, Majeti Narasimha Vara Prasad, Luis Izquierdo Lopez, Dominika Vešelényiová, Maria Rachele Ceccarini, Bernard Fioretti, Gergana P. Ilieva, M. Cerkez Ergoren, Noursaid Tligui, Serghei Sprincean, Saharuddin Bin Mohamad, Miklos Kellermayer, Esra Arslan Ates, Pembe Savas, Ivana Márová, Martin Koller, Hakan Gurkan, Erhan Parıltay, Ercument Ovalı, Sevda Yesim Ozdemir, Tanil Kocagoz, Hilmi Tozkir, Gunnur Demircan, Eda Tahir Turanli, Havva Cobanogullari, Victor Nedovic, Victor Revin, Recep Eroz, Annika Joy Meitern, Munis Dundar, published by European Biotechnology Thematic Network Association
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.