(311b) Fighting Bacteria: A Computational Exploration of Bacterial Interactions

Tackling issues such as antimicrobial resistance and biomedical material interface interactions requires a deep and fundamental understanding of microbial behaviour. In particular, interactions between microbial communities containing multiple species are complex and not yet well understood, even though they are known to influence microbial survival and disease development. For example, the presence of Pseudomonas aeruginosa and Staphylococcus aureus in chronic wounds or the airways of individuals with cystic fibrosis has been associated with shortened life spans and decreased lung function. Understanding the movement and interactions of multispecies microbial communities is vital to develop and implement applications relevant to medical and industrial systems, such as infection treatment strategies. This fundamental insight may also provide knowledge required to describe and design systems in which microbes perform a beneficial purpose, such as bioremediation and filtration.

In this work we use computational methods to probe the fundamental interactions of microbial communities. We extend an existing agent-based model (Storck et al., Biophys. J. 106:2037, 2014; van Holthe et al., arxiv, 2020) of self-propelling bacteria to incorporate molecule secreting and sensing as well as polydisperse bacteria shapes. Using this extended model we study the collective dynamics of a bacterial community containing two species. The results are compared to recent experimental work by Limoli et al. (eLife 8:e47365, 2019) visualising the early interactions between a multispecies community of bacteria relevant to clinical situations, such as respiratory infections and chronic wounds. The most striking interaction is a ‘fighting’ process in which P. aeruginosa, rod-shaped bacteria, sense secreted products from S. aureus, sphere-shaped bacteria, and change their behaviour from collective movement to single-cell exploration with increased velocity. This transition results in a dynamic situation in which the P. aeruginosa surround and ‘attack’ the S. aureus groups and significantly inhibit growth of these sphere-shaped bacteria.

This work provides a tool to probe the fundamental controlling mechanisms and resulting spatiotemporal patterns of diverse multispecies microbial communities. From the results, we can learn how we could rationally manipulate interspecies behaviour during infections. Consequently, we might prevent bacteria with harmful effects (such as P. aeruginosa and S. aureus within the airways of cystic fibrosis patients) from engaging in physical interactions, or bring species together who synergise to produce a beneficial effect.