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Abstract
Natural products derived from microorganisms are essential sources for pharmaceuticals, biofuels, and industrial chemicals. However, their biosynthetic pathways are often limited by complex regulatory networks and low production yields. This dissertation focuses on engineering and applying transcription factor-based biosensor systems to optimize natural product biosynthesis. By integrating experimental and computational approaches, we characterized and developed biosensors responsive to key metabolic intermediates, enabling efficient dynamic pathway regulation in natural product biosynthetic routes. Specifically, we engineered a series of transcription factor-based biosensors to detect and respond to fluctuations in intermediate and central metabolism through rational design and directed evolution. These biosensors were leveraged to construct feedback-regulated genetic circuits, dynamically optimizing pathway flux and mitigating metabolic bottlenecks. Computational modeling, including molecular dynamics simulations and protein-ligand docking, provided structural insights into biosensor-ligand interactions, guiding the rational improvement of sensor specificity and sensitivity. These engineered biosensor systems were applied in Escherichia. coli to enhance the production of coumarin derivatives and saccharides, demonstrating improved pathway efficiency and yield. This work expands the synthetic biology toolkit for metabolic engineering, offering scalable strategies for fine-tuned biosynthetic control. By integrating computational and experimental methodologies, this research advances the development of adaptive genetic tools, paving the way for more efficient and sustainable natural product biosynthesis.