Literature DB >> 18623600

Modeling biocide action against biofilms.

P S Stewart1, M A Hamilton, B R Goldstein, B T Schneider.   

Abstract

A phenomenological model of biocide action against microbial biofilms was derived. Processes incorporated in the model include bulk flow in and out of a well-mixed reactor, transport of dissolved species into the biofilm, substrate consumption by bacterial metabolism, bacterial growth, advection of cell mass within the biofilm, cell detachment from the biofilm, cell death, and biocide concentration-dependent disinfection. Simulations were performed to analyze the general behavior of the model and to perform preliminary sensitivity analysis to identify key input parameters. The model captured several general features of antimicrobial agent action against biofilms that have been observed widely by experimenters and practitioners. These included (1) rapid disinfection followed by biofilm regrowth, (2) slower detachment than disinfection, and (3) reduced susceptibility of microorganisms in biofilms. The results support the plausibility of a mechanism of biofilm resistance in which the biocide is neutralized by reaction with biofilm constituents, leading to a reduction in the bulk biocide concentration and, more significantly, biocide concentration gradients within the biofilm. Sensitivity experiments and analyses identified which input parameters influence key response variables. Each of three response variables was sensitive to each of the five input parameters, but they were most sensitive to the initial biofilm thickness and next most sensitive to the biocide disinfection rate coefficient. Statistical regression modeling produced simple equations for approximating the response variables for situations within the range of conditions covered by the sensitivity experiment. The model should be useful as a tool for studying alternative biocide control strategies. For example, the simulations suggested that a good interval between pulses of biocide is the time to minimum thickness. (c) 1996 John Wiley & Sons, Inc.

Year:  1996        PMID: 18623600     DOI: 10.1002/(SICI)1097-0290(19960220)49:4<445::AID-BIT12>3.0.CO;2-9

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  7 in total

1.  Modeling antibiotic tolerance in biofilms by accounting for nutrient limitation.

Authors:  Mark E Roberts; Philip S Stewart
Journal:  Antimicrob Agents Chemother       Date:  2004-01       Impact factor: 5.191

2.  A three-dimensional computer model of four hypothetical mechanisms protecting biofilms from antimicrobials.

Authors:  Jason D Chambless; Stephen M Hunt; Philip S Stewart
Journal:  Appl Environ Microbiol       Date:  2006-03       Impact factor: 4.792

3.  Challenges of biofilm control and utilization: lessons from mathematical modelling.

Authors:  Paulina A Dzianach; Gary A Dykes; Norval J C Strachan; Ken J Forbes; Francisco J Pérez-Reche
Journal:  J R Soc Interface       Date:  2019-06-12       Impact factor: 4.118

Review 4.  Continuum and discrete approach in modeling biofilm development and structure: a review.

Authors:  M R Mattei; L Frunzo; B D'Acunto; Y Pechaud; F Pirozzi; G Esposito
Journal:  J Math Biol       Date:  2017-07-24       Impact factor: 2.259

5.  Modelling biofilm-induced formation damage and biocide treatment in subsurface geosystems.

Authors:  C C Ezeuko; A Sen; I D Gates
Journal:  Microb Biotechnol       Date:  2012-11-20       Impact factor: 5.813

6.  Spatiotemporal pharmacodynamics of meropenem- and tobramycin-treated Pseudomonas aeruginosa biofilms.

Authors:  Janus Haagensen; Davide Verotta; Liusheng Huang; Joanne Engel; Alfred M Spormann; Katherine Yang
Journal:  J Antimicrob Chemother       Date:  2017-12-01       Impact factor: 5.790

7.  Disturbance frequency determines morphology and community development in multi-species biofilm at the landscape scale.

Authors:  Kim Milferstedt; Gaëlle Santa-Catalina; Jean-Jacques Godon; Renaud Escudié; Nicolas Bernet
Journal:  PLoS One       Date:  2013-11-26       Impact factor: 3.240

  7 in total

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