Issue 89, 2017, Issue in Progress

An experiment-based model quantifying antimicrobial activity of silver nanoparticles on Escherichia coli

Abstract

Silver nanoparticles (AgNPs) are well known to exhibit antimicrobial effects through plausible interactions with proteins and/or deoxyribonucleic acid inside the bacteria. Yet a quantitative understanding on the antimicrobial activities of AgNPs remains obscure. Here we conducted in-depth kinetic growth assays and colony-forming unit (CFU) assays on Escherichia coli (E. coli) cultured in AgNPs or Ag ion-containing growth media. Compared to the Ag-absent culture medium, it was found that the growth rate of the bacteria remained unaffected but the lag time of the bacterial growth was extended due to the presence of AgNPs or Ag ions. From the CFU-based time-kill curves, we observed that fractions of E. coli were killed exponentially in the presence of AgNPs or Ag ions. Based on the experimental data, a quantitative model was established to describe the antimicrobial activity of AgNPs. The predictions from this model agree well with the experimental results. We also showed that the parameters in our model as well as their dependence on the concentrations of Ag and bacteria could, in turn, be determined experimentally. It is expected that our quantitative model and the associated parameters provide an alternative means to minimum inhibitory concentration values for characterizing the antimicrobial activities of Ag ions and AgNPs.

Graphical abstract: An experiment-based model quantifying antimicrobial activity of silver nanoparticles on Escherichia coli

Supplementary files

Article information

Article type
Paper
Submitted
21 Sep 2017
Accepted
02 Dec 2017
First published
12 Dec 2017
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2017,7, 56173-56182

An experiment-based model quantifying antimicrobial activity of silver nanoparticles on Escherichia coli

M. A. Haque, R. Imamura, G. A. Brown, V. R. Krishnamurthi, I. I. Niyonshuti, T. Marcelle, L. E. Mathurin, J. Chen and Y. Wang, RSC Adv., 2017, 7, 56173 DOI: 10.1039/C7RA10495B

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