AI-Driven Metabolic Engineering for Sustainable Microbial Rubber Production
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P118Keywords:
Artificial Intelligence, Microbial Rubber Production, Metabolic Engineering, Machine Learning, Bioprocess OptimizationAbstract
The increasing global demand for rubber, coupled with environmental concerns associated with conventional plantation-based and petrochemical-derived rubber production, has intensified the search for sustainable and scalable alternatives. The potential of microbial rubber biosynthesis has come up as a viable alternative as it can be controlled, it is less land-dependent, and it may have lesser environmental impact. Nevertheless, the classic methods of microbial engineering are limited in many cases by incomplete knowledge of pathways, low productivity, and the use of time-consuming trial-and-error methods. Recent developments in artificial intelligence (AI), machine learning and computational metabolic engineering have dramatically changed this picture. The predictive modeling of complex metabolic networks, rational strain design and intelligent optimization of genetic and enzymatic pathways involved in the biosynthesis of rubber are made possible by AI-led methodologies. Moreover, the combination of AI and the high-throughput screening platforms and real-time bioprocess control systems has resulted in fast strain selection and enhancing fermentation performance, allowing the process to achieve industrial scalability. The article is a review of AI-based metabolic engineering approach to make microbial rubber production sustainable, encompassing the development of pathway forecasts, multi-omics data-docking, and tailored bioprocess engineering. Such issues as data quality, model interpretability, and scale-up are critically discussed, and new opportunities emerging with explainable AI, autonomous biofoundries, and AI-synthetic biology co-design are discussed. Connecting information technology and biotechnology, AI-based solutions provide a revolutionary solution to create environmentally sustainable, economically feasible, and industrially scalable microbial systems of rubber production.
References
1. Kothapalli, K. R. V., Tejani, J. G., & Rajani Pydipalli, R. (2021). Artificial Intelligence for Microbial Rubber Modification: Bridging IT and Biotechnology. Journal of Fareast International University, 4(1), 7-16.
2. Basik, A. A., Sanglier, J. J., Yeo, C. T., & Sudesh, K. (2021). Microbial degradation of rubber: Actinobacteria. Polymers, 13(12), 1989.
3. Steinbüchel, A. (2003). Production of rubber-like polymers by microorganisms. Current Opinion in Microbiology, 6(3), 261-270.
4. Chi, J., Shu, J., Li, M., Mudappathi, R., Jin, Y., Lewis, F., ... & Gu, H. (2024). Artificial intelligence in metabolomics: A current review. TrAC Trends in Analytical Chemistry, 178, 117852.
5. Helmy, M., Smith, D., & Selvarajoo, K. (2020). Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metabolic engineering communications, 11, e00149.
6. Method for manufacturing natural rubber, google patents, online. https://patents.google.com/patent/US8293817B2/en
7. Vennapusa, S. C. R., Tejani, J. G., Mohammed, M. A., & Yarlagadda, V. K. (2024). Automated Robotics Solutions for Precision Molding in Rubber Manufacturing. NEXG AI Review of America, 5(1), 1-18.
8. The Essence of Rubber Production, Kerala Museum, 2023. Online. https://keralamuseum.org/article/capturing-the-essence-of-rubber-production/
9. Nair, K. P. (2020). Rubber (Hevea brasiliensis). In Tree Crops: Harvesting Cash from the World's Important Cash Crops (pp. 287-332). Cham: Springer International Publishing.
10. How is Rubber made?, J.Flex Rubber Products. online. https://www.j-flex.com/how-is-rubber-made/
11. Di Deus, E. (2019). The tree that responds: taming the rubber tree. Vibrant: Virtual Brazilian Anthropology, 16, e16551.
12. How is Natural Rubber Made?, Apple Rubber, 2023. Online. https://www.applerubber.com/blog/how-is-natural-rubber-made/
13. Deepthi, S. R., DSouza, R. M. D., & Shri, K. A. (2020, September). Automated Rubber tree tapping and latex mixing machine for quality production of natural rubber. In 2020 IEEE-HYDCON (pp. 1-4). IEEE.
14. Men, X., Wang, F., Chen, G. Q., Zhang, H. B., & Xian, M. (2018). Biosynthesis of natural rubber: current state and perspectives. International Journal of Molecular Sciences, 20(1), 50.
15. Processing and Grading of Technically Specified Rubber (TSR), agriculture.institute, 2023. Online. https://agriculture.institute/post-harvest-mgt-value-addition/processing-grading-technically-specified-rubber/
16. Hu, Z., Qian, J., Wang, Y., & Ye, C. (2024). Current Status and Applications of Genome‐Scale Metabolic Models of Oleaginous Microorganisms. Food Bioengineering, 3(4), 492-511.
17. Rubber manufacturing process|Natural Rubber Production, Online. https://www.youtube.com/watch?v=DkaFN9s72W
18. Contreras-Salgado, E. A., Sánchez-Morán, A. G., Rodríguez-Preciado, S. Y., Sifuentes-Franco, S., Rodríguez-Rodríguez, R., Macías-Barragán, J., & Díaz-Zaragoza, M. (2024). Multifaceted applications of synthetic microbial communities: advances in biomedicine, bioremediation, and industry. Microbiology Research, 15(3), 1709-1727.
19. Francisco, F. R., Aono, A. H., Da Silva, C. C., Gonçalves, P. S., Scaloppi Junior, E. J., Le Guen, V., ... & Souza, A. P. D. (2021). Unravelling rubber tree growth by integrating GWAS and biological network-based approaches. Frontiers in plant science, 12, 768589.
20. Men, X., Wang, F., Chen, G.-Q., Zhang, H.-B., & Xian, M. (2018). Biosynthesis of natural rubber: Current state and perspectives. International Journal of Molecular Sciences, 19(1), Article 22. https://doi.org/10.3390/ijms19010022.
