In Silico Evaluation of the Impacts of Quorum Sensing Inhibition (QSI) on Strain Competition and Development of QSI Resistance.

As understanding of bacterial regulatory systems and pathogenesis continues to increase, QSI has been a major focus of research. However, recent studies have shown that mechanisms of resistance to quorum sensing (QS) inhibitors (QSIs) exist, calling into question their clinical value. We propose a computational framework that considers bacteria genotypes relative to QS genes and QS-regulated products including private, quasi-public, and public goods according to their impacts on bacterial fitness. Our results show (1) QSI resistance spreads when QS positively regulates the expression of private or quasi-public goods. (2) Resistance to drugs targeting secreted compounds downstream of QS for a mix of private, public, and quasi-public goods also spreads. (3) Changing the micro-environment during treatment with QSIs may decrease the spread of resistance. At fundamental-level, our simulation framework allows us to directly quantify cell-cell interactions and biofilm dynamics. Practically, the model provides a valuable tool for the study of QSI-based therapies, and the simulations reveal experimental paths that may guide QSI-based therapies in a manner that avoids or decreases the spread of QSI resistance.