Mathematical modelling of therapies targeted at bacterial quorum sensing

Anguige, Keith; King, John R.; Ward, J. P.; Williams, Paul

doi:10.1016/j.mbs.2004.06.008

Cited by 57 publications

(61 citation statements)

References 18 publications

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“…The model consists of seven ODEs for AgrC, AgrA, SarA, and complexes. Steady states and their stability, including a bifurcation analysis (in terms of the AIP concentration), were studied which showed hysteresis [typical of QS systems, Anguige et al (2004), Dockery and Keener (2001), Fagerlind et al (2003), James et al (2000)]. According to their model, inhibitory AIP delays activation of the agr system.…”

Section: Models Of the Qs Molecular Mechanismmentioning

confidence: 99%

“…It is also, however, a great source of debate with many publications (Hense et al 2007;Platt and Fuqua 2010;Redfield 2002;Stacy et al 2012;West et al 2012) dedicated to questioning the current understanding of the process which underlays the term quorum sensing, introduced by Fuqua et al (1994). Anguige et al (2004Anguige et al ( , 2005 Bioluminescence Vibrio fischeri James et al (2000), Kuttler and Hense (2008), Romero-Campero and Pérez-Jiménez (2008), Perez et al (2011), Müller et al (2008) AHL…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Modelling of Bacterial Quorum Sensing: A Review

2016

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Bacterial quorum sensing (QS) refers to the process of cell-to-cell bacterial communication enabled through the production and sensing of the local concentration of small molecules called autoinducers to regulate the production of gene products (e.g. enzymes or virulence factors). Through autoinducers, bacteria interact with individuals of the same species, other bacterial species, and with their host. Among QS-regulated processes mediated through autoinducers are aggregation, biofilm formation, bioluminescence, and sporulation. Autoinducers are therefore "master" regulators of bacterial lifestyles. For over 10 years, mathematical modelling of QS has sought, in parallel to experimental discoveries, to elucidate the mechanisms regulating this process. In this review, we present the progress in mathematical modelling of QS, highlighting the various theoretical approaches that have been used and discussing some of the insights that have emerged. Modelling of QS has benefited almost from the onset of the involvement of experimentalists, with many of the papers which we review, published in non-mathematical journals. This review therefore attempts to give a broad overview of the topic to the mathematical biology community, as well as the current modelling efforts and future challenges.B Judith Pérez-Velázquez

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Section: Models Of the Qs Molecular Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical Modelling of Bacterial Quorum Sensing: A Review

2016

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“…Such strategies under current investigation include the development of agents that soak up freely diffusing QSMs or interfere with the QSP process in some way, thus preventing up regulation and virulence; the mathematical modelling of such therapies is currently being undertaken [1]. Motivated by the prediction that only a fraction of a population will become up-regulated when Θ > 0 and either the NF or SU mechanism is operating, we investigate the effectiveness of a (putative) drug that targets bacteria expressing a particular gene during virulence.…”

Section: Implications To Therapymentioning

confidence: 99%

Cell-signalling repression in bacterial quorum sensing

Ward

King²,

Koerber³

et al. 2004

Mathematical Medicine and Biology

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In this paper we expand on two mathematical models for investigating the role of three distinct repression mechanisms within the so called quorum sensing (QS) cell-signalling process of bacterial colonies growing (1) in liquid cultures and (2) in biofilms. The repression mechanisms studied are (i) reduction of cell signalling molecule (QSM) production by a constitutively produced agent degrading the messenger RNA of a crucial enzyme (QSE), (ii) lower QSM production rate due to a negative feedback process and (iii) loss of QSMs by binding directly to a constitutively produced agent; the first two mechanisms are known to be employed by the pathogenic bacterium Pseudomonas aeruginosa and the last is relevant to the plant pathogen Agrobacterium tumefaciens. The modelling approach assumes that the bacterial colony consists of two sub-populations, namely down-and up-regulated cells, that differ in the rates at which they produce QSMs, while QSM concentration governs the switching between sub-populations. Parameter estimates are obtained by curve-fitting experimental data (involving P. aeruginosa growth in liquid culture, obtained as part of this study) to solutions of model (1). Asymptotic analysis of the model (1) shows that mechanism (i) is necessary, but not sufficient, to predict the observed saturation of QSM levels in an exponentially growing colony; either mechanism (ii) or (iii) also needs to be incorporated to obtain saturation. Consequently, only a fraction of the population will become up-regulated. Furthermore, only mechanisms (i) and (iii) effect the main timescales for up regulation. Repression was found to play less significant role in a biofilms, but mechanisms (i)-(iii) were nevertheless found to reduce the ulitimate up-regulated cell fraction and mechanisms (i) and (iii) increase the timescale for substantial up regulation and decrease the wave speed of an expanding front of QS activity.

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“…Now, many other species are observed to exhibit quorum sensing behavior, including major human pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. Quorum sensing has received more and more attention (see [4][5][6][7][8][9][10][11][12][13][14][15] and the references therein) and some models are formulated to investigate its effect on the transmission of disease. Braselton and Waltman [4] formulated the dynamically allocated inhibitor production.…”

Section: Introductionmentioning

confidence: 99%

“…Fergola et al [7] formulated an allelopathic competition model in which a distributed delay term simulates quorum sensing which regulates the delay production process of allelochemicals, and they proved the unique existence of the positive solution and the stability of biologically meaningful steady-state solutions. Anguige et al [9] constructed a multiphase mathematical model of quorum sensing in a maturing Pseudomonas aeruginosa biofilm to investigate the effect of antiquorum sensing and antibiotic treatments on the exopolysaccharide concentration, signal level, bacterial numbers, and biofilm growth rate. The above articles leave out the immune response to the bacterial invasion.…”

Section: Introductionmentioning

confidence: 99%

Stability and Sensitive Analysis of a Model with Delay Quorum Sensing

Zhang

Suo

Zhang

2015

Abstract and Applied Analysis

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This paper formulates a delay model characterizing the competition between bacteria and immune system. The center manifold reduction method and the normal form theory due to Faria and Magalhaes are used to compute the normal form of the model, and the stability of two nonhyperbolic equilibria is discussed. Sensitivity analysis suggests that the growth rate of bacteria is the most sensitive parameter of the threshold parameter 0 and should be targeted in the controlling strategies.

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Mathematical modelling of therapies targeted at bacterial quorum sensing

Cited by 57 publications

References 18 publications

Mathematical Modelling of Bacterial Quorum Sensing: A Review

Mathematical Modelling of Bacterial Quorum Sensing: A Review

Cell-signalling repression in bacterial quorum sensing

Stability and Sensitive Analysis of a Model with Delay Quorum Sensing

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