Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems
International Journal on Software Tools for Technology Transfer (STTT)
A Model Checking Approach to the Parameter Estimation of Biochemical Pathways
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
A unifying framework for modelling and analysing biochemical pathways using Petri nets
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Petri nets for modelling metabolic pathways: a survey
Natural Computing: an international journal
Survey: Computational challenges in systems biology
Computer Science Review
Panel on grand challenges for modeling and simulation
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Multiscale Modeling and Analysis of Planar Cell Polarity in the Drosophila Wing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Modelling across multiple scales is a current challenge in Systems Biology, especially when applied to multicellular organisms. In this paper we present an approach to model at different spatial scales, using the new concept of hierarchically coloured Petri Nets (HCPN). We apply HCPN to model a tissue comprising multiple cells hexagonally packed in a honeycomb formation in order to describe the phenomenon of Planar Cell Polarity (PCP) signalling in Drosophila wing. We illustrate different levels of abstraction that can be used in order to assist the systematic modelling of such a complex system involving intra- and inter-cellular signalling mechanisms, and we provide a design pattern for similar modelling problems. Our initial model describes normal, wild-type PCP signalling, and we illustrate the power of our approach by easily adapting it to various tissue sizes and to describe the phenotype of a well-documented genetic mutation in Drosophila. We have performed a series of analyses on our models which require computational experiments over very large underlying models. All results are reproducible.