Jun Li1, Liang Wang2, Yoshifumi Hashimoto3, Chenyu Tsao2, Thomas K. Wood4, and William E. Bentley5. (1) Center for Biosystem Research & Chemical and Biomolecular Department, University of Maryland, College Park, MD 20742, (2) University of Maryland Biotechnology Institute, College Park, College Park, MD 20742, (3) University of Maryland, College Park, 6143 Plant Science Bldg, College Park, MD 20742, (4) Artie McFerrin Department of Chemical Engineering, Texas A&M University, 200 Jack E. Brown Building, MS 3122, College Station, TX 77843-3122, (5) Center for Biosystem Research & Chemical and Biomolecular Engineering Department, University of Maryland Biotechnology Institute, College Park, 6140 Plant Science building, Univeristy of Maryland, College Park, MD 20742
Quorum sensing (QS) has emerged as an important determinant of bacterial phenotype. Pathogenicity, biofilm formation, and conjugation are among the many factors regulated by intricate and multimodal QS signal transduction processes. In particular, the LuxS/AI-2 QS system has received intense scrutiny due to its conserved nature among Eubacteria, consequently AI-2 has been described as a “universal” signal molecule. In order to understand the hierarchical organization of this genetic circuit, a comprehensive approach incorporating stochastic simulations was developed. We have investigated the synthesis, uptake, and regulation of AI-2, developed testable hypotheses, and revealed new insight on this genetic circuit: (1) mRNA transcript and protein levels of AI-2 synthases, Pfs and LuxS, do not contribute to the dramatically increased level of AI-2 found when cells are grown in the presence of glucose; (2) a concomitant increase in metabolic flux through this synthesis pathway in the presence of glucose only partially accounts for this difference. We predicted that “high-flux” alternative pathways or additional biological steps are involved in AI-2 synthesis; and (3) subsequent experimental results validate this hypothesis. This work demonstrates the utility of linking cell physiology with systems-based stochastic models that can be assembled de novo with partial knowledge of biochemical pathways.