Modeling microbial heterogeneity under antibiotic treatment

University Of Florida, Gainesville FL

Investigators

Abstract

The main goal of this research project is to extend the traditional mathematical framework for modeling microbial kinetics to a systematic study of heterogeneous bacterial populations. New applications from dynamical systems, ordinary and partial differential equations, and stochastic processes will be developed to formulate and study a new class of mathematical models focusing on physiological aspects determining bacterial heterogeneity. The models will be scrutinized using a combination of analytical and numerical methods, and compared to experimental data in the literature. This project will not only advance the general mathematical theory of microbial populations, but it will also produce a number of theoretical conclusions and predictions presented in a way that would be useful for biologists studying microbial heterogeneity in the laboratory setting. This research project will serve as a training ground for future interdisciplinary scientists. The principal investigator will develop a research-based set of educational materials in dynamics of heterogeneous bacterial populations. The evolution and spread of pathogenic bacteria that are genetically resistant to antibiotics plays a significant role in the failure of antibacterial chemotherapy. However, for many acute and chronic bacterial infections neither treatment failure nor the extensive time needed for antibiotics to be effective can be attributed to inherited (genetic) drug resistance in the bacterial populations. Genetically homogenous populations of bacteria are in fact heterogeneous because they include subpopulations that for various physiological and ecological reasons are refractory to antibiotics. Frequently, bacterial populations are also heterogeneous with respect to their metabolism. This research project will integrate the modeling, experimental and analytic approaches into a comprehensive theory describing the physiologically heterogeneous bacterial populations. The resulting theory will aid in optimizing the efficiency of antibacterial drug treatment and develop a sound quantitative basis for estimating optimal drug dosages. Another application of the new theory will include the rational design and control of industrial bioreactors and wastewater treatment plants.