AI-Driven Metabolic Engineering for Microbial Rubber Conversion: IT-enabled Strategies
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Abstract
To improve efficiency and scalability, this study investigates the integration of artificial intelligence (AI) and IT-enabled techniques for the microbial conversion of rubber waste in metabolic engineering. The primary goals are to build synthetic biology constructs for enhanced rubber degradation, optimize bioprocess parameters through IT techniques, and use computational tools for route optimization. Methodologically, the study synthesizes insights from AI-driven techniques and IT-enabled procedures through an extensive analysis of existing literature and secondary data sources. Notable discoveries underscore the progress made in synthetic biology design, bioprocess optimization, and pathway prediction, highlighting the transformative potential of AI-driven metabolic engineering for sustainably produced rubber. The consequences of the policy include the need for more funding for research infrastructure, capacity building, and regulatory monitoring to enable the ethical use and responsible deployment of AI technologies in biotechnology and to remove any technological implementation impediments. This work advances sustainable approaches to resource recovery and waste management for rubber, tackles global environmental issues, and advances the circular economy goal.
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