Hyper-Efficient Transient Protein Production Optimization via Adaptive Multi-objective Bayesian Optimization of Plasmid Scaffold Design

**Abstract:** Current transient protein expression systems suffer from variable yields and inconsistent quality reliant on cell line-specific and batch-dependent factors. This paper introduces a novel framework, Adaptive Multi-objective Bayesian Optimization for Plasmid Scaffold Design (AMBOP-PSD), to optimize transient protein production by systematically evolving plasmid scaffold architecture based on real-time cellular response data. Leveraging machine learning, the system autonomously identifies optimal promoter regions, codon usage strategies, and enhancer elements to maximize protein yield and quality while minimizing off-target effects in a computationally efficient manner, paving the way for standardized and scalable biomanufacturing processes.