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MODELING THE POPULATION DYNAMICS OF ANTIBIOTIC-RESISTANT BACTERIA:: AN AGENT-BASED APPROACH.

Authors :
MURPHY, JAMES T.
WALSHE, RAY
DEVOCELLE, MARC
Source :
International Journal of Modern Physics C: Computational Physics & Physical Computation; Mar2009, Vol. 20 Issue 3, p435-457, 23p, 1 Color Photograph, 3 Diagrams, 2 Charts, 5 Graphs
Publication Year :
2009

Abstract

The response of bacterial populations to antibiotic treatment is often a function of a diverse range of interacting factors. In order to develop strategies to minimize the spread of antibiotic resistance in pathogenic bacteria, a sound theoretical understanding of the systems of interactions taking place within a colony must be developed. The agent-based approach to modeling bacterial populations is a useful tool for relating data obtained at the molecular and cellular level with the overall population dynamics. Here we demonstrate an agent-based model, called Micro-Gen, which has been developed to simulate the growth and development of bacterial colonies in culture. The model also incorporates biochemical rules and parameters describing the kinetic interactions of bacterial cells with antibiotic molecules. Simulations were carried out to replicate the development of methicillin-resistant S. aureus (MRSA) colonies growing in the presence of antibiotics. The model was explored to see how the properties of the system emerge from the interactions of the individual bacterial agents in order to achieve a better mechanistic understanding of the population dynamics taking place. Micro-Gen provides a good theoretical framework for investigating the effects of local environmental conditions and cellular properties on the response of bacterial populations to antibiotic exposure in the context of a simulated environment. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01291831
Volume :
20
Issue :
3
Database :
Complementary Index
Journal :
International Journal of Modern Physics C: Computational Physics & Physical Computation
Publication Type :
Academic Journal
Accession number :
37234590
Full Text :
https://doi.org/10.1142/S0129183109013765