Luminescence control in the marine bacterium Vibrio fischeri : an analysis of the dynamics of lux regulation 1 1Edited by D. E. Draper

James, S. R.; Nilsson, Patric; James, Geoffrey; Kjelleberg, Staffan; Fagerström, Torbjörn

doi:10.1006/jmbi.1999.3484

Cited by 79 publications

(92 citation statements)

References 19 publications

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“…Under these conditions, certain behavior is efficiently performed by the quorum, such as bioluminescence, which is the best known model for understanding the mechanism of cell-density-dependent gene expression. In this section, we will describe a hybrid system model that captures the changes in dynamics of the biochemical reactions observed in the literature [13,16,17].…”

Section: Quorum Sensing In Bacteriamentioning

confidence: 99%

“…First we analyze a previously published plasmid-based genetic network that was designed and synthesized using three repressor transcription factors where one repressor negatively regulates the production of a subsequent repressor [7]. Then we model a biologically important genetic network that controls the quorum sensing response, an adaptive response of certain gram negative bacteria to local population density [13,17]. The quorum sensing response controls the luminescent behavior in certain strains of Vibrio which has been linked to the normal development of the bacterial host [18] as well as to medically important phenomena such as biofilm formation by Pseudomonas aerugenosa, an organism that can cause overwhelming pneumonia and septic shock [11,20].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Modeling and Simulation of Biomolecular Networks

Alur¹,

Belta²,

Ivančić³

2001

Lecture Notes in Computer Science

150

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Abstract. In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by a network of chemical reactions in which regulatory proteins can control genes that produce other regulators, which in turn control other genes. Further, the feedback pathways appear to incorporate switches that result in changes in the dynamic behavior of the cell. This paper describes a hybrid systems approach to modeling the intra-cellular network using continuous differential equations to model the feedback mechanisms and mode-switching to describe the changes in the underlying dynamics. We use two case studies to illustrate a modular approach to modeling such networks and describe the architectural and behavioral hierarchy in the underlying models. We describe these models using Charon [2], a language that allows formal description of hybrid systems. We provide preliminary simulation results that demonstrate how our approach can help biologists in their analysis of noisy genetic circuits. Finally we describe our agenda for future work that includes the development of models and simulation for stochastic hybrid systems.

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Section: Quorum Sensing In Bacteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling and Simulation of Biomolecular Networks

Alur¹,

Belta²,

Ivančić³

2001

Lecture Notes in Computer Science

150

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“…On the other hand, mathematical modeling can significantly aid and complement experimental methods in answering biological questions that involve spatial and temporal scales of the QS phenomenon. Some aspects of either intracellular [10][11][12][13] or population [14][15][16] dynamics have been mathematically modeled to gain insight into the QS phenomenon in Pseudomonas aeruginosa and Vibrio fischeri. However, because of the lack of detailed molecular information, experimentally testable conclusions on the connections between intracellular and population dynamics have rarely been made.…”

Section: Introductionmentioning

confidence: 99%

Transition to Quorum Sensing in an Agrobacterium Population: A Stochastic Model

Goryachev¹,

Toh²,

Wee³

et al. 2005

PLoS Comp Biol

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Understanding of the intracellular molecular machinery that is responsible for the complex collective behavior of multicellular populations is an exigent problem of modern biology. Quorum sensing, which allows bacteria to activate genetic programs cooperatively, provides an instructive and tractable example illuminating the causal relationships between the molecular organization of gene networks and the complex phenotypes they control. In this work we-to our knowledge for the first time-present a detailed model of the population-wide transition to quorum sensing using the example of Agrobacterium tumefaciens. We construct a model describing the Ti plasmid quorum-sensing gene network and demonstrate that it behaves as an ''on-off'' gene expression switch that is robust to molecular noise and that activates the plasmid conjugation program in response to the increase in autoinducer concentration. This intracellular model is then incorporated into an agent-based stochastic population model that also describes bacterial motion, cell division, and chemical communication. Simulating the transition to quorum sensing in a liquid medium and biofilm, we explain the experimentally observed gradual manifestation of the quorum-sensing phenotype by showing that the transition of individual model cells into the ''on'' state is spread stochastically over a broad range of autoinducer concentrations. At the same time, the population-averaged values of critical autoinducer concentration and the threshold population density are shown to be robust to variability between individual cells, predictable and specific to particular growth conditions. Our modeling approach connects intracellular and population scales of the quorum-sensing phenomenon and provides plausible answers to the long-standing questions regarding the ecological and evolutionary significance of the phenomenon. Thus, we demonstrate that the transition to quorum sensing requires a much higher threshold cell density in liquid medium than in biofilm, and on this basis we hypothesize that in Agrobacterium quorum sensing serves as the detector of biofilm formation.

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“…The QS process has been the subject of a number mathematical models, the approaches ranging competition models [3], through mass-action-based kinetic models of the biochemistry [5,9,15,18] to macro-scale population models [16,25,26]. The mass action model of Dockery and Keener [9], modelling QS in P. aeruginosa is (more-or-less) as depicted in Figure 1, considered, inter alia, (a) the decay of the mRNA-lasI protein required to generate QSMs, reflecting the BI mechanism, (b) an inhibitor of mRNA-lasI, reflecting NF, and (c) the constant decay of QSMs, reflecting SU; we note, however, that there is no biological evidence that P. aeruginosa produces a molecule that soaks-up or cause decay of QSMs, so it is uncertain whether the SU process is relevant for this bacterium.…”

Section: Introductionmentioning

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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Luminescence control in the marine bacterium Vibrio fischeri : an analysis of the dynamics of lux regulation 1 1Edited by D. E. Draper

Cited by 79 publications

References 19 publications

Hybrid Modeling and Simulation of Biomolecular Networks

Hybrid Modeling and Simulation of Biomolecular Networks

Transition to Quorum Sensing in an Agrobacterium Population: A Stochastic Model

Cell-signalling repression in bacterial quorum sensing

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