Inhibition of quorum sensing in a computational biofilm simulation

J.A Fozard,M Lees,J.R King,B.S Logan

doi:10.1016/j.biosystems.2012.02.002

Abstract

Bacteria communicate through small diffusible molecules in a process known as quorum sensing. Quorum-sensing inhibitors are compounds which interfere with this, providing a potential treatment for infections associated with bacterial biofilms. We present an individual-based computational model for a developing biofilm. Cells are aggregated into particles for computational efficiency, but the quorum-sensing mechanism is modelled as a stochastic process on the level of individual cells.Simulations are used to investigate different treatment regimens. The response to the addition of inhibitor is found to depend significantly on the form of the positive feedback in the quorum-sensing model; in cases where the model exhibits bistability, the time at which treatment is initiated proves to be critical for the effective prevention of quorum sensing and hence potentially of virulence.

Highlights

Under certain environmental conditions, planktonic bacteria form multicellular structures known as biofilms on solid surfaces or at air-fluid interfaces
In keeping with the above goals, we summarise the objectives of the paper as follows: we seek to explore the effectiveness of quorum sensing inhibitors in terms of their dependence on the properties of the quorum sensing network, the population size and the time of and level of exposure to inhibitors, with the intention of providing insight into what treatment protocols may be most effective
We use our simulations to investigate quorum sensing in a developing biofilm and the treatment of such a biofilm with a quorumsensing inhibitor (QSI)

Summary

Introduction

Planktonic bacteria (suspended in fluid) form multicellular structures known as biofilms on solid surfaces or at air-fluid interfaces. This is similar to that described in Picioreanu et al (2004); differences include the incorporation here of (i) EPS production (note that EPS production is included in Xavier et al (2005a), Kreft et al (2001) and Lardon et al (2011)) (ii) a quorum-sensing model, which requires particles to keep track of the number and states of the cells that they contain, and (iii) the use of a simpler voxel-based shoving model for biomass spreading.

Results

Conclusion