Design/CPN - A Computer Tool for Coloured Petri Nets
TACAS '97 Proceedings of the Third International Workshop on Tools and Algorithms for Construction and Analysis of Systems
Petri Nets as Token Objects: An Introduction to Elementary Object Nets
ICATPN '98 Proceedings of the 19th International Conference on Application and Theory of Petri Nets
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 systems and synthetic biology
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
MARCIE - Model Checking and Reachability Analysis Done EffiCIEntly
QEST '11 Proceedings of the 2011 Eighth International Conference on Quantitative Evaluation of SysTems
Survey: Computational challenges in systems biology
Computer Science Review
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Modeling 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 Colored 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) signaling in Drosophila wing. We have constructed a family of related models, permitting different hypotheses to be explored regarding the mechanisms underlying PCP. In addition our models include the effect of well-studied genetic mutations. We have applied a set of analytical techniques including clustering and model checking over time series of primary and secondary data. Our models support the interpretation of biological observations reported in the literature.