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|Title:||Spatial quorum sensing modelling using coloured hybrid Petri nets and simulative model checking|
|Keywords:||quorum sensing;bio lm formation;coloured hybrid Petri nets;coloured stochastic Petri nets;di usion in 3D space;simulative model checking|
|Abstract:||Background: Quorum sensing drives bio lm formation in bacteria in order to ensure that bio lm formation only occurs when colonies are of a su cient size and density. This spatial behaviour is achieved by the broadcast communication of an autoinducer in a di usion scenario. This is of interest, for example, when considering the role of gut microbiota in gut health. This behaviour occurs within the context of the four phases of bacterial growth, speci cally in the exponential stage (phase 2) for autoinducer production and the stationary stage (phase 3) for bio lm formation. Results: We have used coloured hybrid Petri nets to step-wise develop a exible computational model for E.coli bio lm formation driven by autoinducer 2 (AI-2) which is easy to con gure for di erent notions of space. The model describes the essential components of gene transcription, signal transduction, extra and intra cellular transport, as well as the two-phase nature of the system. We build on a previously published non-spatial stochastic Petri net model of AI-2 production, keeping the assumptions of a limited nutritional environment, and our spatial hybrid Petri net model of bio lm formation, rst presented at the NETTAB 2017 workshop. First we consider the two models separately without space, and then combined, and nally we add space. We describe in detail our step-wise model development and validation. Our simulation results support the expected behaviour that bio lm formation is increased in areas of higher bacterial colony size and density. Our analysis techniques include behaviour checking based on linear time temporal logic. Conclusions: The advantages of our modelling and analysis approach are the description of quorum sensing and associated bio lm formation over two phases of bacterial growth, taking into account bacterial spatial distribution using a exible and easy to maintain computational model. All computational results are reproducible.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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