In-vivo distributed multicellular control of gene expression in microbial consortia

Abstract

This paper presents an innovative approach in synthetic biology, focusing on the engineering of biomolecular feedback control systems within microbial consortia to achieve robust and precise regulation of desired phenotypes. Traditional biomolecular control strategies, while effective, are predominantly confined to single-cell applications, limiting their complexity and adaptability due to metabolic constraints. We propose a novel methodology that distributes control functionalities across different cell populations within a microbial consortium. This approach leverages the division of labor and cooperative interactions among microbial populations, significantly enhancing flexibility and robustness. We build on the foundation of synthetically engineered microbial consortia, known for their applications in complex compound production and advanced computational processes. However, the challenge of implementing a distributed feedback control loop for precise phenotype regulation in synthetic communities has remained unaddressed. Our work closes this gap by developing and validating an in-vivo feedback control loop distributed across a bacterial consortium, specifically designed using two Escherichia coli strains. These strains are engineered to perform the three essential control functions: computation, sensing, and actuation. We employ orthogonal quorum sensing signaling molecules for communication between the controller and target cell populations. Through in-vivo experiments, we compare closed-loop and open-loop configurations to demonstrate the effectiveness of our approach. The results show that our distributed control system can regulate gene expression levels in target populations with high precision and robustness, even in the presence of consortium composition variations. This feature is particularly vital for practical applications in synthetic biology where stability and adaptability are crucial. Our study not only addresses a significant gap in synthetic biology literature but also opens avenues for more complex and reliable synthetic biology applications.