AI-Driven Microbial Strain Optimization for Biotech Applications

In the competitive landscape of biotechnology, optimizing microbial strains for various applications is critical. Our SME has identified a need to enhance strain selection processes to improve yields and efficiency in biomanufacturing. The project involves developing an AI-driven platform that leverages machine learning models such as TensorFlow and PyTorch to analyze large datasets and predict the most promising strains for production. By integrating OpenAI's NLP capabilities and Langchain for data processing, the solution will provide insights into metabolic pathways and genetic factors affecting productivity. YOLO and computer vision will assist in real-time monitoring and analysis. The platform aims to reduce time-to-market, lower costs, and enhance product quality by providing precise strain recommendations. The project will take 12-16 weeks, with a focus on creating a scalable and user-friendly interface for biotech professionals.