Most bacteria in natural and clinical settings grow as surface-attached biofilms, which are bacterial communities that have self-assembled into an encased matrix. One mode of surface motility, called swarming, is observed in cells that are propelled by rotating flagella, by the secretion of slime, and by retracting type IV pili. Study of swarming is particularly important because its regulation is controlled by combination of complex and variable multi-scale events. While swarming, a bacterial community may move in a large-scale coordinated pattern exceeding the size of individual bacterium by orders of magnitude depending upon the gene expression of individual cells, the sensing of chemical signals present in a hydrating environment, and the physical characteristics influencing the attached bacterial cells. To date, the most advanced modeling efforts of bacterial motility have focused on single levels or scales, e.g., genomic/proteomic, cellular and population. The bacterium Pseudomonas aeruginosa is an opportunistic human pathogen that causes skin, eye, lung, and gastrointestinal infections in susceptible individuals. We propose to study P. aeruginosa swarming by developing and integrating models from micro-scales to macro-scales to analyze bacterial motility in concert with laboratory experiments. Integration between scales will lead to a much deeper understanding of the universal or generic features of biological phenomena and how simultaneous processes at different scales interact. The main hypothesis of this proposed research is that bacteria coordinate cell density and cooperation to maximize surface motility which requires assimilation of population, nutrient, and physical cues by these cells. Because identification of cell interactions is extremely difficult experimentally, we will use multi-scale models to perform predictive simulations describing complex bacterial interactions that potentially control swarming. Study of the mechanisms of bacterial pattern formation will help identify the key interactions between cells, describe a mechanism of bacterial surface colonization, and provide knowledge for engineering and controlling bacterial growth on surfaces. A key aspect of this work will be to compare the predictions obtained in silico with experimental observations to calibrate the model and use the model to generate new biological hypotheses to be tested experimentally. Of particular interests to be examined are the influence of motility patterns, surface liquid properties, and cell-cell physical interactions required for Pseudomonas aeruginosa swarming. Our proposed iterative approach will use multiscale model simulations and laboratory experiments to describe variations to swarming with alterations to motility and rhamnolipid production in combination with laboratory examination of isogenic mutants deficient in certain motility modes or rhamnolipid production. This work will allow us to determine how bacteria efficiently colonize surfaces by coordinating their motion and rhamnolipid production over time.

Public Health Relevance

Most infections are the result of surface-attached biofilm communities of bacteria that colonize host surfaces. Pseudomonas aeruginosa is an opportunistic pathogen responsible for both acute and persistent infections in susceptible individuals, as exampled by those for burn victims and people with cystic fibrosis. A key aspect of these infections is the formation of bacterial swarms, which are surface-associated, socially organized communities of cells. Because identification of single cell behavior within groups is extremely difficult experimentally, we will use multiscale models to perform predictive simulations describing complex bacterial interactions that potentially control swarming. This combined multiscale modeling and laboratory study of bacterial behavior on surfaces will provide new critical information needed for the eradication, prevention and treatment of the P. aeruginosa infections.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Lyster, Peter
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University of Notre Dame
Biostatistics & Other Math Sci
Schools of Arts and Sciences
Notre Dame
United States
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