Monday, May 13, 2024
5/13/2024
Project Summary As the progress on the development of biosensors, genetic control tools and genetic circuits in the past few years enabled advanced and sophisticated dynamic control of biosynthesis for optimal production performance in bacteria, its lack of broad applicability in enhancing natural product biosynthesis is becoming into a major problem since such dynamic control highly relies on specific biosensors. However, such biosensors are not always available for most natural products. Within this MIRA research program, the PI aims to bridge these gaps by developing platforms with broad applicability in improving biosynthesis of a broad spectrum of natural products including polysaccharides, terpenoids and plant polyketides in bacteria. To reach this goal, the following four coherent themes will be investigated: 1) engineering and characterizing a series of biosensors for monitoring and responding to central metabolism in bacteria; 2) developing dynamic control strategies to manage conflict between growth and production, which can serve as a platform to support efficient biosynthesis of polysaccharides, human milk oligosaccharides and glycosylated compounds; 3) developing dynamic control strategies to improve product yield by reducing carbon loss, which can serve as a platform to support high-level biosynthesis of terpenoids and many other acetyl-CoA derived products; 4) developing dynamic control strategies to coordinate precursor supplies, which could serve as a platform to support efficient biosynthesis of plant polyketides. The proposed research program is expected to advance dynamic control of natural product biosynthesis in bacteria to a whole new level. With these studies, we will greatly enhance the availability of biosensors targeting central metabolism. We will gain new insights into carbon flux distribution in cellular metabolism at various conditions through biosensor-aided observation and further understand the interaction of central metabolism with heterologous biosynthesis during the dynamic processes. Furthermore, the knowledge gained in this research program will continue to advance the understanding of how to seamlessly implement artificial logics and functions in native cellular context.
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