A mathematical model of disrupting cell-to-cell communication by bacteria

Overview

The emergence of resistance of bacteria to antibiotics presents a global healthcare challenge that intensifies the search for strategies to increase the effcacy of therapy. Several mechanisms are involved in resistance of bacteria against antibiotics such as mutations in genes, horizontal gene transfer, and biofilm formation. Bacteria can communicate with each other through production and response to local concentration of small molecules called autoinducers.

This mechanism is called quorum sensing (QS).It has been suggested that QS can influence the resistance of bacteria against antibiotics in different ways, directly or indirectly. For example, it can regulate the secretion of Extracellular Polymeric Substances (EPS) that encloses bacteria and protects them physically. Vice versa, antibiotics are a stressor for the bacteria, and QS has been characterized as a method of stress response, forcing the bacteria for example to more cooperation and consequently more resistance.

A promising new strategy to cope with QS associated problems is QS blockade. Disruption of QS can be done in several ways such as inhibiting QS signal production and blocking the QS receptors of cell (QS inhibition), or accelerating the degradation of QS signals (quorum quenching (QQ)). QQ molecules appear naturally and are used by microbial species to gain an advantage in competitive environments. For example QQ has been successfully employed to reduce the pathogenicity of common plant pathogens, membrane biofouling in wastewater treatment plants, and to control bacterial infections.

Using QQ as a safe alternative to control QS associated issues and an adjuvant for antibiotic treatment is quite a new strategy and there are unknown aspects of its influence on biological systems and involved mechanisms that need to be explored. In particular, less is known about interaction between species producing signal molecules and bacteria with QQ enzymes in spatially distributed mixtures of species. Fortunately, we can use mathematical models to explore these aspects, address questions arising in the fields related to public health, and see how QQ can be used beneficially.

Outcomes

You will use a set of Ordinary Dierential Equations (ODEs) to describe growth of bacteria that produce autoinducers and bacteria with QQ enzymes and interaction between them. Using linear stability analysis you will study the long term behaviour of the system. Moreover, you will learn that since the resulting model is nonlinear, steady states are not easily found and a combination of analytical and numerical techniques must be applied.

Furthermore, you will use computer simulations to identify key parameters that influence the involved interaction with focus on QS disruption. Some interesting questions that can be addressed are whether the system can have stable existence of anon-trivial steady state; it is possible, for certain model parameters, for the system to have mixed steady states.

Skills and experience

A good understanding of ODE and some proficiency in MATLAB is expected.

Keywords

• Mathematics
• Biology
• Bacteria
• Differential equations

Contact

Contact Adrianne for more details adrianne.jenner@qut.edu.au