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Dynamical Model of Drug Accumulation in Bacteria: Sensitivity Analysis and Experimentally Testable Predictions

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  • Neda Vesselinova
  • Boian S Alexandrov
  • Michael E Wall

Abstract

We present a dynamical model of drug accumulation in bacteria. The model captures key features in experimental time courses on ofloxacin accumulation: initial uptake; two-phase response; and long-term acclimation. In combination with experimental data, the model provides estimates of import and export rates in each phase, the time of entry into the second phase, and the decrease of internal drug during acclimation. Global sensitivity analysis, local sensitivity analysis, and Bayesian sensitivity analysis of the model provide information about the robustness of these estimates, and about the relative importance of different parameters in determining the features of the accumulation time courses in three different bacterial species: Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The results lead to experimentally testable predictions of the effects of membrane permeability, drug efflux and trapping (e.g., by DNA binding) on drug accumulation. A key prediction is that a sudden increase in ofloxacin accumulation in both E. coli and S. aureus is accompanied by a decrease in membrane permeability.

Suggested Citation

  • Neda Vesselinova & Boian S Alexandrov & Michael E Wall, 2016. "Dynamical Model of Drug Accumulation in Bacteria: Sensitivity Analysis and Experimentally Testable Predictions," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-20, November.
    • Handle: RePEc:plo:pone00:0165899
    DOI: 10.1371/journal.pone.0165899
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    References listed on IDEAS

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    1. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
    2. Saltelli, A. & Andres, T. H. & Homma, T., 1993. "Sensitivity analysis of model output : An investigation of new techniques," Computational Statistics & Data Analysis, Elsevier, vol. 15(2), pages 211-238, February.
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