Model-Based Optimization of a qCRISPRi Circuit for Dynamic Control of Metabolic Pathways

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Systems Biology

Background:

• Metabolic engineering for sustainable chemical production faces challenges with metabolic burdens impacting cell viability and productivity.
• Dynamic control strategies, like quorum sensing (QS)-based circuits, offer solutions by autonomously regulating gene expression based on cell density.

Purpose of the Study:

• To investigate a QS-regulated CRISPR interference (qCRISPRi) circuit for dynamic control of metabolic pathways.
• To evaluate the impact of leaky expression and regulator stringency on circuit performance, focusing on switching characteristics.

Main Methods:

• Mathematical modeling was employed to analyze the influence of promoter leakiness and LuxR stringency on gene expression dynamics.
• Experimental validation was conducted in *E. coli* to confirm model predictions regarding circuit behavior.

Main Results:

• High promoter leakiness in dCas9 expression led to reduced switching density and premature gene repression.
• A high-stringency LuxR variant improved switching precision by minimizing leakiness and enabling sharper transitions in the qCRISPRi circuit.
• Experimental results in *E. coli* validated that increased LuxR stringency enhances dynamic circuit performance.

Conclusions:

• LuxR stringency is a critical parameter for optimizing the performance of QS-based dynamic control systems.
• This study provides a quantitative framework and generalizable design principles for implementing dynamic control in metabolic engineering applications.