Literature DB >> 22374433

Inhibition of quorum sensing in a computational biofilm simulation.

J A Fozard1, M Lees, J R King, B S Logan.   

Abstract

Bacteria communicate through small diffusible molecules in a process known as quorum sensing. Quorum-sensing inhibitors are compounds which interfere with this, providing a potential treatment for infections associated with bacterial biofilms. We present an individual-based computational model for a developing biofilm. Cells are aggregated into particles for computational efficiency, but the quorum-sensing mechanism is modelled as a stochastic process on the level of individual cells. Simulations are used to investigate different treatment regimens. The response to the addition of inhibitor is found to depend significantly on the form of the positive feedback in the quorum-sensing model; in cases where the model exhibits bistability, the time at which treatment is initiated proves to be critical for the effective prevention of quorum sensing and hence potentially of virulence.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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Year:  2012        PMID: 22374433     DOI: 10.1016/j.biosystems.2012.02.002

Source DB:  PubMed          Journal:  Biosystems        ISSN: 0303-2647            Impact factor:   1.973


  9 in total

Review 1.  Resistance to quorum-quenching compounds.

Authors:  Rodolfo García-Contreras; Toshinari Maeda; Thomas K Wood
Journal:  Appl Environ Microbiol       Date:  2013-09-06       Impact factor: 4.792

Review 2.  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

3.  A biophysical limit for quorum sensing in biofilms.

Authors:  Avaneesh V Narla; David Bruce Borenstein; Ned S Wingreen
Journal:  Proc Natl Acad Sci U S A       Date:  2021-05-25       Impact factor: 11.205

4.  Interactions among quorum sensing inhibitors.

Authors:  Rajat Anand; Navneet Rai; Mukund Thattai
Journal:  PLoS One       Date:  2013-04-23       Impact factor: 3.240

5.  BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.

Authors:  Thomas E Gorochowski; Antoni Matyjaszkiewicz; Thomas Todd; Neeraj Oak; Kira Kowalska; Stephen Reid; Krasimira T Tsaneva-Atanasova; Nigel J Savery; Claire S Grierson; Mario di Bernardo
Journal:  PLoS One       Date:  2012-08-24       Impact factor: 3.240

6.  In Silico Evaluation of the Impacts of Quorum Sensing Inhibition (QSI) on Strain Competition and Development of QSI Resistance.

Authors:  Guopeng Wei; Chieh Lo; Connor Walsh; N Luisa Hiller; Radu Marculescu
Journal:  Sci Rep       Date:  2016-10-13       Impact factor: 4.379

Review 7.  Agent-based modelling in synthetic biology.

Authors:  Thomas E Gorochowski
Journal:  Essays Biochem       Date:  2016-11-30       Impact factor: 8.000

8.  Rule-based regulatory and metabolic model for Quorum sensing in P. aeruginosa.

Authors:  Nadine S Schaadt; Anke Steinbach; Rolf W Hartmann; Volkhard Helms
Journal:  BMC Syst Biol       Date:  2013-08-21

9.  Inoculation density and nutrient level determine the formation of mushroom-shaped structures in Pseudomonas aeruginosa biofilms.

Authors:  Azadeh Ghanbari; Jaber Dehghany; Timo Schwebs; Mathias Müsken; Susanne Häussler; Michael Meyer-Hermann
Journal:  Sci Rep       Date:  2016-09-09       Impact factor: 4.379

  9 in total

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