(689f) Dynamic, Multi-Scale Simulations of Pathogenic Populations in Semisolid Gels Integrating Genome-Scale Metabolic Models of Pseudomonas Aeruginosa.

Pseudomonas aeruginosa (PA) is a robust pathogen that readily establishes dense microbial communities once it infiltrates vulnerable biological interfaces such as the diseased mucus layer above the lung epithelium. Their large genome gives cells an arsenal of different phenotypic behaviors e.g., virulence factor production and enhanced antibiotic resistance that increase the PA population’s pathogenicity in their infection niche. This expression heterogeneity is further compounded by the dynamic metabolite conditions PA encounters in the external environment as the population grows and depletes local nutrients. At the macroscopic level, these metabolite conditions are also driven by the evolving spatial distribution of the population as PA grows and spreads through the gel-like mucus layer via active bacterial motility. To more effectively bridge the coupled intracellular and environmental phenomena length scales and better simulate community level outputs, we present a computational workflow design utilizing a continuum mechanics model system informed by coarse-grained metabolite flux predictions from a PA14 genome-scale metabolic model (GEM) [1].

Continuum and GEM model parameters were constrained to experimental measurements of PA14 in low-density agar motility assay, or swim plate, platforms, which mimic PA spreading through soft gel environments such as diseased respiratory mucus. Simulation predictions were then assessed on their overall biomass accumulation and the production of cytotoxic pyocyanin by the PA14 population under different external gel compositions. Additionally, we determined coarse-grained metabolic flux predictions from the PA14 GEM following in-silico gene knockouts in order to assess the efficacy of inhibiting gene for growth and pyocyanin under different cell motility conditions. Model simulations integrating these in-silico knockout GEMs were validated using single-transposon PA14 mutants. As our multi-scale model is informed by these experiment conditions, we aim to better extend the steady-state predictions of the PA14 GEM to the heterogenous microbial community structures that arise as the pathogen colonizes the gel environment.

[1] Payne, D. D. et al. (2021). An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism. Npj Systems Biology and Applications, 7(1). https://doi.org/10.1038/s41540-021-00198-2