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.