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Relating switching rates between normal and persister cells to substrate and antibiotic concentrations: a mathematical modelling approach supported by experiments.


ABSTRACT: We developed and compared two mathematical models of variable phenotypic switching rates between normal and persister cells that depend on substrate concentration and antibiotic presence. They could be used to simulate the formation of persisters in environments with concentration gradients such as biofilms. Our models are extensions of a previous model of the dynamics of normal and persistent cell populations developed by Balaban et al. (2004, Science 305: 1622). We calibrated the models' parameters with experimental killing curves obtained after ciprofloxacin treatment of samples regularly harvested from planktonic batch cultures of Klebsiella pneumoniae. Our switching models accurately reproduced the dynamics of normal and persistent populations in planktonic batch cultures and under antibiotic treatment. Results showed that the models are valid for a large range of substrate concentrations and for zero or high doses of antibiotics.

SUBMITTER: Carvalho G 

PROVIDER: S-EPMC5658594 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Relating switching rates between normal and persister cells to substrate and antibiotic concentrations: a mathematical modelling approach supported by experiments.

Carvalho Gabriel G   Guilhen Cyril C   Balestrino Damien D   Forestier Christiane C   Mathias Jean-Denis JD  

Microbial biotechnology 20170721 6


We developed and compared two mathematical models of variable phenotypic switching rates between normal and persister cells that depend on substrate concentration and antibiotic presence. They could be used to simulate the formation of persisters in environments with concentration gradients such as biofilms. Our models are extensions of a previous model of the dynamics of normal and persistent cell populations developed by Balaban et al. (2004, Science 305: 1622). We calibrated the models' param  ...[more]

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