CAREER: Programmable microbial community composition and function using plasmid conjugation

University Of Rochester, Rochester NY

Investigators

Abstract

Microbial communities hold transformative potential for bioproduction, environmental cleanup, and human health. However, controlling the balance of bacterial species within these communities to ensure they function effectively remains a major challenge. This project seeks to overcome these barriers by uncovering principles that enable microbial populations to self-regulate and remain stable, inspired by how bacteria naturally exchange genetic information. Using modeling and experiments, these principles will be applied to develop and demonstrate a practical approach for controlling microbial communities. The results will have broad implications for agriculture, healthcare, and environmental science, providing tools to manipulate microbiomes in ways that are reliable and predictable. Beyond the research, this project emphasizes education and accessibility by creating hands-on learning kits that help students visualize and understand complex biological systems. These kits will be used in classrooms and made freely available to teachers and students, opening the door to exciting opportunities in STEM for learners of all backgrounds. By combining cutting-edge science with inclusive education, this work aims to address critical challenges and inspire the next generation of innovators. Engineering stable, precisely controlled microbial populations remains a fundamental challenge in synthetic biology. This project addresses these challenges by developing an innovative platform that leverages horizontal gene transfer (HGT) to self-regulate and stabilize microbial consortia. Specifically, this project will use plasmid conjugation to dynamically balance growth rates and community composition, creating programmable microbial populations capable of maintaining stability across diverse conditions. Four core objectives will be integrated to achieve stability in this way. First, rigorous computational modeling will identify the key parameters governing HGT dynamics and their impact on microbial stability. Second, synthetic engineering methods will be leveraged to design and test modular plasmids with optimized transfer and maintenance characteristics to experimentally validate these models. Third, the platform will be evaluated in real-world scenarios by applying it to microbial-enabled production of curcumin, a valuable natural product. Finally, the project will emphasize education and accessibility by creating hands-on biomodeling kits that combine physical components and software simulations to teach core systems biology concepts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.