A simple model for the early events of quorum sensing in Pseudomonas aeruginosa: modeling bacterial swarming as the movement of an "activation zone"

Netotea, Sergiu; Bertani, Iris; Steindler, Laura; Kerényi, Ádám; Venturi, Vittorio; Pongor, Sándor

doi:10.1186/1745-6150-4-6

Cited by 34 publications

(34 citation statements)

References 21 publications

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“…swimming, foraging) Tang et al (2007), van Gestel et al (2012) Virulence Pseudomonas aeruginosa Dockery and Keener (2001), Fagerlind et al (2003Fagerlind et al ( , 2005, Viretta and Fussenegger (2004), Netotea et al (2009) AHL, AHQ Koerber et al (2005), Jabbari et al (2010), Gustafsson et al (2004) AIP Escherichia coli Li et al (2006) AI-2, Indole Lee et al (2007) See Taga and Bassler (2003), Miller and Bassler (2001) and West et al (2012) for a more comprehensive list. Note that not all models concentrate on a particular bacterium…”

Section: Hunter Et Al (2013) Ahlmentioning

confidence: 99%

“…Another important result of their work is that the speed of the travelling wave of QS activation increases with the diffusion rate of the QS signal and decreases with the decay rate of the QS signal. Netotea et al (2009) presented an 2D agent-based computational model for the onset of swarming in P. aeruginosa, which is controlled by threshold levels of AHL signals and secreted factors (public goods). Their experimental framework utilizes swarming agar plates that allow the growth of activated bacteria, but not of non-activated ones.…”

Section: Bioluminescencementioning

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: Hunter Et Al (2013) Ahlmentioning

confidence: 99%

Section: Bioluminescencementioning

confidence: 99%

Mathematical Modelling of Bacterial Quorum Sensing: A Review

2016

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“…We share the widespread opinion that the "change of bacterial lifestyle" is crucial for colonizing habitats and infecting susceptible hosts -unfortunately the signalling systems that orchestrate the underlying communication and collaboration mechanisms are not accurately annotated in bacterial genomes. Therefore, a systematic characterization of QS systems in Gram negative bacteria was carried out [68,69] and a modelling effort to map out the theoretically possible consequences of communication and collaboration in bacterial populations was initiated [70][71][72]. Virulence and adaptability of many Gram-negative bacterial species are associated with an N-acylhomoserine lactone (AHL) gene regulation mechanism called quorum sensing (QS).…”

Section: Bacterial Communication and Cooperation In Health And Diseasementioning

confidence: 99%

Clinical Proteomics and Omics Clues Useful in Translational Medicine Research

2014

Omics in Clinical Practice

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Abstract:The currently hyped expectation of personalized medicine is often associated with just achieving the information technology led integration of biomolecular sequencing, expression and histopathological bioimaging data with clinical records at the individual patients' level as if the significant biomedical conclusions would be its more or less mandatory result. It remains a sad fact that many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known and, thus, most of the molecular and cellular data cannot be interpreted in terms of biomedically relevant conclusions. Whereas the historical trend will certainly be into the general direction of personalized diagnostics and cures, the temperate view suggests that biomedical applications that rely either on the comparison of biomolecular sequences and/or on the already known biomolecular mechanisms have much greater chances to enter clinical practice soon. In addition to considering the general trends, we exemplarily review advances in the area of cancer biomarker discovery, in the clinically relevant characterization of patient-specific viral and bacterial pathogens (with emphasis on drug selection for influenza and enterohemorrhagic E. coli) as well as progress in the automated assessment of histopathological images. As molecular and cellular data analysis will become instrumental for achieving desirable clinical outcomes, the role of bioinformatics and computational biology approaches will dramatically grow.Author summary: With DNA sequencing and computers becoming increasingly cheap and accessible to the layman, the idea of integrating biomolecular and clinical patient data seems to become a realistic, short-term option that will lead to patient-specific diagnostics and treatment design for many diseases such as cancer, metabolic disorders, inherited conditions, etc. These hyped expectations will fail since many, if not most biomolecular mechanisms that translate the human genomic information into phenotypes are not known yet and, thus, most of the molecular and cellular data collected will not lead to biomedically relevant conclusions. At the same time, less spectacular biomedical applications based on biomolecular sequence comparison and/or known biomolecular mechanisms have the potential to unfold enormous potential for healthcare and public health. Since the analysis of heterogeneous biomolecular data in context with clinical data will be increasingly critical, the role of bioinformatics and computational biology will grow correspondingly in this process.

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“…A quorum érzékelés hatékony modellezéséhez azonban szükség van újabb, a sejtek által termelt kémiai komponensek, a kommunikációs jel és a közjavak reprezentációjára, melyek szabadon diffundálnak a környezetben. Így munkánk során egy olyan hibrid ágens modellt fejlesztettünk ki, mely egy kvalitatív szempontból helyes quorum érzékelési modellnek tekinthető [107]. A modell részletes bemutatására az Módszerek 3.4.…”

Section: Gerlee Modell (Sejtautomata Modell)unclassified

“…A Netotea-féle hibrid rendszer [107] első verziója Java programozási nyelven íródott, melyet a későbbiekben MATLAB programozási környezetben fejlesztettünk tovább. A két verzió ugyanazon algoritmus alapján működik, viszont a MATLAB verzió tervezése során kiemelt figyelmet fordítottunk a forráskód optimalizációjára, mely által a szimulációk is gyorsabbak lett.…”

A simple model for the early events of quorum sensing in Pseudomonas aeruginosa: modeling bacterial swarming as the movement of an "activation zone"

Abstract: Background: Quorum sensing (QS) is a form of gene regulation based on cell-density that depends on inter-cellular communication. While there are a variety of models for bacterial colony morphology, there is little work linking QS genes to movement in an open system.

Cited by 34 publications

References 21 publications

Mathematical Modelling of Bacterial Quorum Sensing: A Review

Mathematical Modelling of Bacterial Quorum Sensing: A Review

Clinical Proteomics and Omics Clues Useful in Translational Medicine Research

A bakteriális kommunikáció vizsgálata ágens alapú modellel

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