Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review
- PMID: 40397295
- DOI: 10.1007/s12010-025-05260-x
Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review
Abstract
The wide application of machine learning has provided more possibilities for biological manufacturing, and the combination of machine learning and synthetic biology technology has ignited even more brilliant sparks, which has created an unpredictable value for the upgrading of microbial cell factories. The review delves into the synergies between machine learning and synthetic biology to create research worth investigating in biotechnology. We explore relevant databases, toolboxes, and machine learning-derived models. Furthermore, we examine specific applications of this combined approach in chemical production, human health, and environmental remediation. By elucidating these successful integrations, this review aims to provide valuable guidance for future research at the intersection of biomanufacturing and artificial intelligence.
Keywords: Biomanufacturing; Database; Machine learning; Microbial cell factory; Synthetic biology; Toolbox.
© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Conflict of interest statement
Declarations. Ethical Approval: This review did not involve animals or human subjects. Consent to Participate: Not applicable. Consent for Publication: Not applicable. Conflict of interest: The authors declare no competing interests.
Similar articles
-
Stabilizing machine learning for reproducible and explainable results: A novel validation approach to subject-specific insights.Comput Methods Programs Biomed. 2025 Sep;269:108899. doi: 10.1016/j.cmpb.2025.108899. Epub 2025 Jun 21. Comput Methods Programs Biomed. 2025. PMID: 40570739
-
Healthcare workers' priorities of WHO snakebite strategic objectives for the control and prevention of snakebite envenoming in Ghana: A machine learning statistical design of experiment modeling.PLoS Negl Trop Dis. 2025 Jul 10;19(7):e0013295. doi: 10.1371/journal.pntd.0013295. eCollection 2025 Jul. PLoS Negl Trop Dis. 2025. PMID: 40638704 Free PMC article.
-
Using nursing data for machine learning-based prediction modeling in intensive care units: A scoping review.Int J Nurs Stud. 2025 Sep;169:105133. doi: 10.1016/j.ijnurstu.2025.105133. Epub 2025 Jun 7. Int J Nurs Stud. 2025. PMID: 40544524
-
Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.Front Public Health. 2025 Jul 18;13:1613946. doi: 10.3389/fpubh.2025.1613946. eCollection 2025. Front Public Health. 2025. PMID: 40756392 Free PMC article.
-
Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic Review.Aesthetic Plast Surg. 2025 Jan;49(1):389-399. doi: 10.1007/s00266-024-04421-3. Epub 2024 Oct 9. Aesthetic Plast Surg. 2025. PMID: 39384606
References
-
- Anteghini, M., dos Santos, V. M., & Saccenti, E. (2021) In-Pero: Exploiting deep learning embeddings of protein sequences to predict the localisation of peroxisomal proteins. International Journal of Molecular Sciences, 22(12), 6409.
-
- Bohr, A. and Memarzadeh, K. (2020), in Artificial Intelligence in Healthcare, (Bohr, A. and Memarzadeh, K., eds.), Academic Press, pp. 25–60.
-
- Cao, R., Freitas, C., Chan, L., Sun, M., Jiang, H., & Chen, Z. (2017) ProLanGO: Protein function prediction using neural machine translation based on a recurrent neural network. Molecules, 22(10), 1732.
-
- Chang, C. Y., Hsu, T. W. and Chang, J. M. (2019) PSLCNN: Protein subcellular localization prediction for eukaryotes and prokaryotes using deep learning. 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 1–5.
Publication types
MeSH terms
Grants and funding
- 2020YFA0908300/National Key R & D Program of China
- No. 22208167/National Natural Science Foundation of China
- No.22308167/National Natural Science Foundation of China
- No. BK20230381/Natural Science Foundation of Jiangsu Province
- BK20233003/the Jiangsu Basic Research Center for Synthetic Biology Grant
LinkOut - more resources
Full Text Sources