Numerical investigation of microbial quorum sensing under various flow conditions

A pore-scale reactive transport modeling study for quorum sensing-driven biofilm dispersal in heterogeneous porous media

Microorganisms regulate the expression of energetically expensive phenotypes via a collective decision-making mechanism known as quorum sensing (QS). This study investigates the intricate dynamics of biofilm growth and QS-controlled biofilm dispersal in heterogeneous porous media, employing a pore-scale reactive transport modeling approach. Model simulations carried out under various fluid flow conditions and biofilm growth scenarios reveal that QS processes are influenced not only by the biomass density of biofilm colonies but also by a complex interplay between pore architecture, flow velocity, and the rates of biofilm growth and dispersal. This study demonstrates that pore architecture controls the initiation of QS processes and advection gives rise to oscillatory growth of biofilms. Such oscillation is suppressed if biofilm dynamics are in favor of sustaining a sufficiently high signal concentration, such as fast growth or slow dispersal rates. By establishing a mathematical framework, this study contributes to the fundamental understanding of QS-controlled biofilm dynamics in complex environments.

Emergent robustness of bacterial quorum sensing in fluid flow

Bacteria use intercellular signaling, or quorum sensing (QS), to share information and respond collectively to aspects of their surroundings. The autoinducers that carry this information are exposed to the external environment; consequently, they are affected by factors such as removal through fluid flow, a ubiquitous feature of bacterial habitats ranging from the gut and lungs to lakes and oceans. To understand how QS genetic architectures in cells promote appropriate population-level phenotypes throughout the bacterial life cycle requires knowledge of how these architectures determine the QS response in realistic spatiotemporally varying flow conditions. Here we develop and apply a general theory that identifies and quantifies the conditions required for QS activation in fluid flow by systematically linking cell- and population-level genetic and physical processes. We predict that when a subset of the population meets these conditions, cell-level positive feedback promotes a robust collective response by overcoming flow-induced autoinducer concentration gradients. By accounting for a dynamic flow in our theory, we predict that positive feedback in cells acts as a low-pass filter at the population level in oscillatory flow, allowing a population to respond only to changes in flow that occur over slow enough timescales. Our theory is readily extendable and provides a framework for assessing the functional roles of diverse QS network architectures in realistic flow conditions.