Validation of Transcription and Translation of Synthetic Exogenous Genes in Nicotiana benthamiana

This project aims to evaluate the expression of exogenous genes designed by artificial intelligence (AI)-based biological large language models in Nicotiana benthamiana, a model plant system widely used in synthetic biology. Using transient expression methods, we will introduce these computationally designed genes into tobacco and systematically analyze their transcriptional (RT-qPCR) and translational (Western blot) performance. The findings will provide critical experimental validation for AI-generated biological sequences, offering insights into the reliability and optimization of machine learning approaches for synthetic gene design. This study will bridge the gap between computational biology and experimental biology, advancing the development of intelligent bioengineering platforms.