Numerical investigation of microbial quorum sensing under various flow conditions

Abstract

Microorganisms efficiently coordinate phenotype expressions through a decision-making process known as quorum sensing (QS). We investigated QS amongst distinct, spatially distributed microbial aggregates under various flow conditions using a process-driven numerical model. Model simulations assess the conditions suitable for QS induction and quantify the importance of advective transport of signaling molecules. In addition, advection dilutes signaling molecules so that faster flow conditions require higher microbial densities, faster signal production rates, or higher sensitivities to signaling molecules to induce QS. However, autoinduction of signal production can substantially increase the transport distance of signaling molecules in both upstream and downstream directions. We present empirical approximations to the solutions of the advection–diffusion–reaction equation that describe the concentration profiles of signaling molecules for a wide range of flow and reaction rates. These empirical relationships, which predict the distribution of dissolved solutes along pore channels, allow to quantitatively estimate the effective communication distances amongst multiple microbial aggregates without further numerical simulations.

Emergent robustness of bacterial quorum sensing in fluid flow

“…Thus, flow can drive heterogeneous phenotypes in genetically identical populations [30]. However, although these important macroscopic aspects of the QS response to fluid flow have been observed experimentally [22][23][24][25] and in simulations [31][32][33][34][35][36][37], it remains unclear how the properties of QS genetic networks, which contain significant differences across species [38], control emergent population-level responses in realistic steady or intermittent flow conditions.…”

Emergent robustness of bacterial quorum sensing in fluid flow

“…In addition to its mechanical effects on the structure of cell populations ( 15 – 19 ), external fluid flow has been found to have a strong influence on the transport of relevant chemicals including nutrients ( 8 , 20 ), antibiotics during host treatment ( 21 , 22 ), and QS AIs ( 23 – 26 ). Recent experimental ( 23 – 27 ) and numerical ( 28 – 34 ) studies suggest that flow-induced AI transport can affect population-level phenotypes by introducing chemical gradients within populations and, if the flow is strong enough, suppressing QS altogether. These results raise two important questions about QS genetic networks.…”