Biotech for a Greener Planet: Sustainable Solutions through Synthetic Biology
Abstract
Synthetic biology offers groundbreaking possibilities for creating environmentally friendly alternatives to traditional industrial processes. This paper investigates the use of engineered microbes and bio-based systems for sustainable production of materials, fuels, and chemicals. We discuss case studies involving biodegradable plastics, carbon capture via algae, and biofuel generation. The research highlights how integrating biology with digital design tools can reduce ecological footprints and support the transition to a bio-based economy.
References
Chittineni, S. (2019). Optimizing Microservices Performance with Reinforcement Learning: A Case Study in Spring Boot Applications. American Journal of AI & Innovation, 1(1).
Chittineni, S. (2019). AI-Driven Optimization of Backend Systems: Enhancing Performance and Reducing Latency in Large-Scale Applications. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Chittineni, S. (2020). Leveraging Machine Learning for Automated Thread Dump Analysis and Performance Tuning in Enterprise Java Applications. Australian Journal of Cross-Disciplinary Innovation, 2(2).
Mohammed, C. (2021). Revolutionizing Financial Operations: A Comprehensive Study on the Impact of SAP and Kyriba Integration. International Journal of Sustainable Development in Computing Science, 3(2), 1-19. Retrieved from https://ijsdcs.com/index.php/ijsdcs/article/view/696/260
The Critical Role of Accurate Balance Carry Forward in Preventing Financial Irregularities. (2022). International Journal of Interdisciplinary Finance Insights, 1(1), 1-13. https://injmr.com/index.php/ijifi/article/view/143
Chittineni, S. (2022). Automated API Performance Testing and Anomaly Detection Using Machine Learning in RESTful Architectures. American Journal of AI & Innovation, 4(4).
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable Algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of "personally identifiable information". Communications of the ACM, 53(6), 24-26.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.