A mechanistic model to study biofilm structure and metabolism in antibiotic activity
Creators: name: Farag, Joseph Emad
Publisher: name: No Publisher Supplied
Year: 2022

description: Bacterial biofilms are aggregates of bacteria that adhere and remain attached to a surface, within a biopolymer matrix that they produce. These biofilms may be seen in a variety of settings across a multitude of disciplines. Biofilms are found in industrial water systems and have been exploited in the production of industrially important chemicals, and they occur in areas of biomedical significance such as in the lungs of cystic fibrosis patients. Within these bacterial biofilms, there are two major factors that contribute to biofilm growth and response to antibiotic treatment: the transport mechanisms and the cellular states. The transport mechanisms include the attachment of bacteria to the biofilm, the detachment of bacteria from the biofilm, the advection of cells in the biofilm, and the diffusion of molecules (substrate, antibiotic, etc.) into and throughout the different layers of the biofilm. At the same time, the cells in the bacterial biofilm may exist in several different cellular states. These states include live cells, transient cells, persister cells, and dead cells. These two factors that affect biofilm growth and response to antibiotic activity are modeled together to create a tool that enables both the better design of experiments to study antibiotic treatment of biofilms as well as the better understanding of experimental results of antibiotic treatment of biofilms. This mechanistic model was developed to capture the spatial and temporal elements of biofilm dynamics using continuum material balances that produce a set of coupled partial differential equations. Because of the diffusion of material into the biofilm that occurs at the interface of the biofilm and the surrounding bulk fluid, solute and biomass concentration gradients emerge in the biofilm. Moving away from the biofilm-bulk fluid interface, the concentration of molecules affecting biofilm growth and response to antibiotic treatment decreases due to the transport barrier. It is essential to describe and well characterize the differences in the concentration gradients prevalent throughout the biofilm, which lead to differences in how the biofilm responds to treatment and how different layers of the biofilm are comprised of different amounts of differing cell types. The developed model is used to simulate several microbiology assays. This is completed to generate a dose-response curve and to identify a minimal biofilm eradication concentration (MBEC) and a minimal biofilm inhibitory concentration (MBIC) for a selected group of parameters that describes a biofilm in a specific context under specified treatment conditions. These assays were simulated under both CDC and perfusion bioreactor conditions. Sensitivity analysis was then performed by variation of parameters related to the saturable pharmacologic kinetics of biofilm disinfection, the rates of conversion to the different cell states, and the rate of influent antibiotic decay in perfusion bioreactor assays. This analysis allows for new insights into biofilm dynamics and antibiotic development. It was discovered that the overall dose-response curve that illustrates the response of the biofilm is highly sensitive to, and mirrors, the saturable kinetics of the biofilm disinfection but with some variation due to this model incorporating transport mechanisms and the existence of multiple cellular states. Additionally, it was also found that the variation of the cellular state rates of conversion does not have as large of an impact on the resultant dose-response curve generated by the model but does play a significant role in the composition of the biofilm cell types after treatment, indicating that there are instances where specialized antibiotics are advised. Variation of the influent antibiotic concentration rate in perfusion bioreactor assay simulations revealed the importance of the availability and sustained presence of antibiotic during the course of biofilm treatment. Going forward, this mechanistic model provides a valuable tool for researchers to study bacterial biofilm structure and metabolism in antibiotic activity. The flexibility of this model allows for ample use of this model to study of biofilms in various settings and contexts under a variety of treatment protocols. This model may be used in conjunction with assays being performed in the laboratory to predict the response of biofilms before treatment and to analyze the results after treatment. The unique intertwining of the two modeling approaches to capture the transport mechanisms seen in bacterial biofilms with the cellular states present allows the model to be a valuable tool in antibiotic development and delivery in bacterial biofilms.