(218g) Employing Continuum Mechanics Models to Capture the Population Motility and Growth of Pseudomonas Aeruginosa in Variable Semisolid Gels

Pseudomonas aeruginosa is a facultative, opportunistic pathogen capable of infiltrating the body at vulnerable interfacial epithelial linings. Notably, severe respiratory infections of P. aeruginosa are observed in patients with the hereditary disease cystic fibrosis (CF), where disrupted fluid transport leads to a thicker lung mucus layer that is more difficult to clear. As a result of reduced mucus clearance, these bacteria can establish chronic infections that exhibit diverse infection behaviors ranging from increased biofilm exopolysaccharide formation to increased antibiotic resistance. Advancements in interpreting and integrating genomic sequencing data have allowed computational approaches to probe how such behaviors arise from single-cell level events. However, few of these models consider how bacterial transport can contribute to the evolving microenvironment. At the population level, bacterial motility has the potential to influence cell density distribution over their environment and consequentially impact the emergence of nutrient gradients that the bacteria experience. Here, we design a continuum mechanics model to simulate the spatiotemporal evolution of P. aeruginosa PA14 driven by motility and growth. We then compare the accuracy of our continuum model outputs to experimental PA14 density profiles expanding through soft agarose gels. Lastly, we utilize our model to investigate how bacterial motility is altered by varying the gel composition with native mucin glycoproteins characteristic of the lung mucus layer. As our continuum model is adjusted for PA14 expansion in these semi-solid environments, we aim to ultimately incorporate information from single-cell models and better connect predictions across multiple length scales.