Predictive genetic circuit design for phenotype reprogramming in plants.

Plants, with intricate molecular networks for environmental adaptation, offer groundbreaking potential for reprogramming with predictive genetic circuits. However, realizing this goal is challenging due to the long cultivation cycle of plants, as well as the lack of reproducible, quantitative methods and well-characterized genetic parts. Here, we establish a rapid (~10 days), quantitative, and predictive framework in plants. A group of orthogonal sensors, modular synthetic promoters, and NOT gates are constructed and quantitatively characterized. A predictive model is developed to predict the designed circuits' behavior accurately. Our versatile and robust framework, validated by constructing 21 two-input circuits with high prediction accuracy (R(2) = 0.81), enables multi-state phenotype control in both Arabidopsis thaliana and Nicotiana benthamiana in response to chemical inducers. Our study achieves predictable design and application of synthetic circuits in plants, offering valuable tools for the rapid engineering of plant traits in biotechnology and agriculture.