AI-Driven Strain Engineering for Enhanced Biocompound Production: From Genome Design to Industrial Bioprocessing

Authors

https://doi.org/10.48313/bic.vi.76

Abstract

The increasing demand for high-value Biocompounds has accelerated the development of engineered microbial cell factories as sustainable platforms for biomanufacturing. However, conventional strain-engineering approaches remain constrained by the complexity of microbial metabolism, nonlinear genotype–phenotype relationships, limited experimental throughput, and difficulties in translating laboratory-scale performance into industrial production. Artificial Intelligence (AI) and Machine Learning (ML) are emerging as powerful approaches for addressing these challenges by enabling data-driven prediction, optimization, and decision-making across multiple stages of Biocompound production. This review provides an integrated overview of AI-driven strain engineering, focusing on the convergence of multi-omics analysis, metabolic modeling, genome engineering, synthetic biology, and bioprocess optimization. The applications of AI in genomics, transcriptomics, proteomics, and metabolomics are discussed in the context of identifying metabolic bottlenecks and predicting genotype–phenotype relationships. Particular attention is given to AI-assisted metabolic pathway discovery, genome-scale metabolic modeling, metabolic flux prediction, precursor and cofactor engineering, CRISPR-based strain design, regulatory-element optimization, and enzyme engineering. The review further examines the use of AI for predicting strain performance, optimizing fermentation conditions, implementing adaptive feeding strategies, monitoring bioprocesses, and facilitating scale-up. The integration of these approaches within Design–Build–Test–Learn (DBTL) cycles is highlighted as a promising framework for accelerating iterative strain development and reducing experimental burden. Finally, current challenges related to data quality, model interpretability, experimental validation, transferability, and industrial implementation are discussed, together with emerging opportunities offered by foundation models, generative AI, digital twins, automated biofoundries, and self-driving laboratories. The convergence of AI and biotechnology is expected to enable increasingly predictive, efficient, and scalable platforms for next-generation Biocompound production.

Keywords:

Artificial intelligence, Machine learning, Strain Engineering, Metabolic engineering, Multi-omics, Synthetic biology, Biocompound

References

  1. [1] Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., ... & Oak, S. (2025). Artificial intelligence index report. https://doi.org/10.48550/arXiv.2504.07139
  2. [2] Gursoy, D., & Cai, R. (2025). Artificial intelligence: An overview of research trends and future directions. International journal of contemporary hospitality management, 37(1), 1–17. https://doi.org/10.1108/IJCHM-03-2024-0322
  3. [3] Gama, F., & Magistretti, S. (2025). Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of AI applications. Journal of product innovation management, 42(1), 76–111. https://doi.org/10.1111/jpim.12698
  4. [4] Svennberg, E., Han, J. K., Caiani, E. G., Engelhardt, S., Ernst, S., Friedman, P., ... & Zorzi, A. (2025). State of the art of artificial intelligence in clinical electrophysiology in 2025: A scientific statement of the European heart rhythm association (EHRA) of the ESC, the heart rhythm society (HRS), and the ESC working group on E-cardiology. Europace, 27(5), EUAF071. https://doi.org/10.1093/europace/euaf071
  5. [5] Eaton, S. E. (2025). Global trends in education: Artificial intelligence, postplagiarism, and future-focused learning for 2025 and beyond-2024-2025 werklund distinguished research lecture. International journal for educational integrity, 21(1), 12. https://doi.org/10.1007/s40979-025-00187-6
  6. [6] Zha, D., Bhat, Z. P., Lai, K. H., Yang, F., Jiang, Z., Zhong, S., & Hu, X. (2025). Data-centric artificial intelligence: A survey. ACM computing surveys, 57(5), 1-42. https://doi.org/10.1145/3711118
  7. [7] Mageed, I. A. (2025). Ad educationem in millionibus annorum antea: The profound impact of artificial intelligence on education. Zenodo preprints. https://doi.org/10.5281/zenodo.16789605
  8. [8] McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in human behavior: Artificial humans, 3, 100121. https://doi.org/10.1016/j.chbah.2025.100121
  9. [9] Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., ... & Wong, L. W. (2025). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of computer information systems, 65(1), 76-107. https://doi.org/10.1080/08874417.2023.2261010
  10. [10] Zhang, K., Yang, X., Wang, Y., Yu, Y., Huang, N., Li, G., ... & Yang, S. (2025). Artificial intelligence in drug development. Nature medicine, 31(1), 45-59. https://doi.org/10.1038/s41591-024-03434-4
  11. [11] Derakhshan, A., Teo, T., & Khazaie, S. (2025). Investigating the usefulness of artificial intelligence-driven robots in developing empathy for English for medical purposes communication: The role-play of Asian and African students. Computers in human behavior, 162, 108416. https://doi.org/10.1016/j.chb.2024.108416
  12. [12] Yim, I. H. Y., & Su, J. (2025). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of computers in education, 12(1), 93–131. https://doi.org/10.1007/s40692-023-00304-9
  13. [13] Faiyazuddin, M., Rahman, S. J. Q., Anand, G., Siddiqui, R. K., Mehta, R., Khatib, M. N., ... & Sah, R. (2025). The impact of artificial intelligence on healthcare: A comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health science reports, 8(1), e70312. https://doi.org/10.1002/hsr2.70312
  14. [14] Xu, T., & Baghaei, S. (2025). Reshaping the future of sports with artificial intelligence: Challenges and opportunities in performance enhancement, fan engagement, and strategic decision-making. Engineering applications of artificial intelligence, 142, 109912. https://doi.org/10.1016/j.engappai.2024.109912
  15. [15] Chai, T., Tao, Y., Zhao, C., & Chen, X. (2025). Hierarchical metabolic engineering for rewiring cellular metabolism. FEMS microbiology reviews, 49, fuaf047. https://doi.org/10.1093/femsre/fuaf047
  16. [16] 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
  17. [17] Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The journal of strategic information systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
  18. [18] Ren, Y., Celinska, E., Cai, P., & Zhou, Y. J. (2025). Combing directed enzyme evolution with metabolic engineering to develop efficient microbial cell factories. Chem & bio engineering, 2(8), 449–459. https://doi.org/10.1021/cbe.5c00002
  19. [19] Woolston, B. M., Edgar, S., & Stephanopoulos, G. (2013). Metabolic engineering: Past and future. Annual review of chemical and biomolecular engineering, 4, 259–288. https://doi.org/10.1146/annurev-chembioeng-061312-103312
  20. [20] Cheng, X., Han, M., Zhang, G., Qiao, W., Wang, L., & Ma, Y. (2026). Metabolic engineering strategies for flavonoid biosynthesis in saccharomyces cerevisiae: Current advances and future perspectives. Biotechnology and bioengineering, 123(8), 2162–2179. https://doi.org/10.1002/bit.70248
  21. [21] Zhou, Y., Liu, Y., Sun, H., & Lu, Y. (2025). Creating novel metabolic pathways by protein engineering for bioproduction. Trends in biotechnology, 43(5), 1094–1103. https://www.cell.com/trends/biotechnology/abstract/S0167-7799(24)00308-1
  22. [22] Ramakrishnan, P., Sundaram, T., Lahiri, D., Nag, M., & Bhattacharya, D. (2025). Genetic engineering and modulation of metabolic pathways. In Introduction to metabolic engineering and application (pp. 295–330). Springer. https://doi.org/10.1007/978-3-031-93189-5_12
  23. [23] Shi, S., Chen, Y., & Nielsen, J. (2025). Metabolic engineering of yeast. Annual review of biophysics, 54(1), 101–120. https://doi.org/10.1146/annurev-biophys-070924-103134
  24. [24] 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
  25. [25] Wu, G., Yan, Q., Jones, J. A., Tang, Y. J., Fong, S. S., & Koffas, M. A. G. (2016). Metabolic burden: Cornerstones in synthetic biology and metabolic engineering applications. Trends in biotechnology, 34(8), 652–664. https://www.cell.com/trends/biotechnology/abstract/S0167-7799(16)00044-5
  26. [26] Lee, J., Yu, H. E., & Lee, S. Y. (2025). Metabolic engineering of microorganisms for carbon dioxide utilization. Current opinion in biotechnology, 91, 103244. https://doi.org/10.1016/j.copbio.2024.103244
  27. [27] Wang, K., Song, X., Cui, B., Wang, Y., & Luo, W. (2025). Metabolic engineering of Escherichia coli for efficient production of ectoine. Journal of agricultural and food chemistry, 73(1), 646–654. https://doi.org/10.1021/acs.jafc.4c07640
  28. [28] Zhang, G., & Wang, J. (2025). Artificial intelligence-driven metabolic engineering is applied to the development of active ingredients in Traditional Chinese Medicine. BIO web of conferences (Vol. 174, p. 03013). EDP Sciences. https://doi.org/10.1051/bioconf/202517403013
  29. [29] Zhao, M., Pan, X., Arsalan, A., Zabed, H. M., Guo, L., Zhang, C., & Qi, X. (2026). Metabolic engineering and synthetic biology-driven strategies to harness microbial production of Adipic acid: Current status and future direction. ACS synthetic biology, 15(3), 893–914. https://doi.org/10.1021/acssynbio.5c00869
  30. [30] Domenzain, I., Lu, Y., Wang, H., Shi, J., Lu, H., & Nielsen, J. (2025). Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast. Proceedings of the national academy of sciences, 122(9), e2417322122. https://doi.org/10.1073/pnas.2417322122
  31. [31] Sokra, I., Somaly, S., & Meta, H. (2026). CRISPR-Cas9-Based genome editing in microbial biotechnology: Advances in metabolic engineering, fermentation systems, and industrial applications. Journal of agriculture and technology, 2(1), 154–166. https://doi.org/10.5281/zenodo.17994411
  32. [32] Panagiotidou, E., & Theodosiou, E. (2026). Systems metabolic engineering: An integrated approach for future environmental biotechnology. Biotechnology for the environment, 3(1), 8. https://doi.org/10.1186/s44314-026-00042-z
  33. [33] Singh, A., & Negi, P. S. (2025). Biotechnological application of health-promising bioactive compounds. In Biotechnological intervention in production of bioactive compounds: Biosynthesis, characterization and applications (pp. 73–94). Springer. https://doi.org/10.1007/978-3-031-76859-0_5
  34. [34] Moss, A., Peh, J. H., Segaran, T. C., Lananan, F., Kari, Z. A., Wei, L. S., ... & Azizi, M. N. (2025). Bioactive compounds in aquaculture. Aquaculture international, 33(6), 414. https://doi.org/10.1007/s10499-025-01985-y
  35. [35] Trujillo-Cayado, L. A., Sánchez-García, R. M., García-Domínguez, I., Rodríguez-Luna, A., Hurtado-Fernández, E., & Santos, J. (2025). Emerging trends in sustainable biological resources and bioeconomy for food production. Applied sciences, 15(12), 6555. https://doi.org/10.3390/app15126555
  36. [36] Szabo, K., Varvara, R.-A., Ciont, C., Macri, A. M., & Vodnar, D. C. (2025). An updated overview on the revalorization of bioactive compounds derived from tomato production and processing by-products. Journal of cleaner production, 497, 145151. https://doi.org/10.1016/j.jclepro.2025.145151
  37. [37] Marzban, N., Psarianos, M., Herrmann, C., Schulz-Nielsen, L., Olszewska-Widdrat, A., Arefi, A., ... & Sturm, B. (2025). Smart integrated biorefineries in bioeconomy: A concept toward zero-waste, emission reduction, and self-sufficient energy production. Biofuel research journal, 12(1), 2319-2349. https://doi.org/10.18331/BRJ2025.12.1.4
  38. [38] Gallego, I. (2025). Biocompounds of commercial interest from freshwater and marine phytoplankton. In The role of plankton in freshwater and marine ecology (pp. 1-23). IntechOpen. https://doi.org/10.5772/intechopen.1008500
  39. [39] Khan Jani, F. (2025). Design and chemical engineering of polymeric biomaterials: Functionalization, crosslinking, and characterization strategies for advanced 3D bioprinting. Biocompounds, 2(3), 151–167. https://doi.org/10.48313/bic.vi.42
  40. [40] Laribi, A., Zieniuk, B., Bouchedja, D. N., Hafid, K., Elmechta, L., & Becila, S. (2025). Valorization of olive mill wastewater via Yarrowia Lipolytica: Sustainable production of high-value metabolites and Biocompounds—A review. Fermentation, 11(6), 326. https://doi.org/10.3390/fermentation11060326

Published

2026-09-13

Issue

Vol. 3 No. 3 (2026): Biocompounds

Section

Articles

How to Cite

saheli, H. . (2026). AI-Driven Strain Engineering for Enhanced Biocompound Production: From Genome Design to Industrial Bioprocessing. Biocompounds, 3(3), 191-217. https://doi.org/10.48313/bic.vi.76

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