Christoph Hethey (1), Charlotte Kloft (2) Wilhelm Huisinga (3)
(1) Institut für Biochemie und Biologie, Universität Potsdam, (2) Institute of Pharmacy, Dept. Clinical Pharmacy & Biochemistry, Freie Universität Berlin, (3) Institut für Mathematik, Universität Potsdam
Objectives: Effects of antibiotics on bacterial growth are typically modeled by a direct change of growth or death rate of a bacterial population. Such an approach rarely allows to account for the mechanism of action in a mechanistic way – which is expected to be particularly relevant for the analysis of synergistic or antagonistic effects of antibiotic drug cocktails. The objective was to exploit known relationships between the population growth rate and the physiological state of a (typical) bacterial cell [1,2] to develop a cell-level population growth model, that allows to account for the effect of an antibiotic drug on the cellular level, causing a change in population growth rate.
Methods: The state of a bacterial cell was characterized by physiological descriptors, including measures for cellular functions like peptide elongation rate, fraction of active ribosomes and RNA polymerase activity. A functional relationship between these variables and the growth rate was estimated from data in [3]. For tetracycline, the pharmacodynamic effect on cell level was included as a change in the peptide elongation rate. Time kill curve data for E. coli were extracted from literature. Parameter estimation and simulations were performed in MATLAB (R2012a).
Results: Based on the functional relationships from cell state to growth rate and vice versa, we showed for a broad range of growth rates (0.3 – 3.0 doublings / h), that the predicted state of a (typical) bacterial cell allows to correctly predict its corresponding growth rate. For E. coli under exposure to constant concentrations of tetracycline, the model predicted time kill curve data in agreement with literature, based on the estimated reference state of a cell from control data.
Conclusions: For E. coli and tetracycline, we successfully showed that a cell-level bacterial growth model can be used to predict the impact of antibiotic perturbation on the population growth rate, i.e., growth and its change under antibiotic exposure is predictable from a cellular state of the bacterial cell. Extensions to include more drug classes are currently under development.
References:
[1] Schaechter, B. Y. M., & Kjeldgaard, N. (1958). Dependency on Medium and Temperature of Cell Size and Chemical Composition during Balanced Growth of Salmonella typhimurium. J. Gen. Microbiol., 19, 592–606
[2] Churchward, G., Bremer, H., & Young, R. (1982). Macromolecular composition of bacteria. J. Theor. Biol., 94(3), pp. 651–70
[3] Bremer, H., & Dennis, P. P. (1996). Modulation of Chemical Composition and Other Parameters of the Cell by Growth Rate. In F. C. Neidhardt (Ed.), Escherichia Coli and Salmonella Typhimurium: Vol 2: Cellular and Molecular Biology (2. ed., pp. 1527–1540)
Reference: PAGE 23 (2014) Abstr 3100 [www.page-meeting.org/?abstract=3100]
Poster: Methodology - New Modelling Approaches