Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Model checking
Verifying Continuous Time Markov Chains
CAV '96 Proceedings of the 8th International Conference on Computer Aided Verification
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
A density-based cluster validity approach using multi-representatives
Pattern Recognition Letters
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
Clustering of time series data-a survey
Pattern Recognition
On the analysis of numerical data time series in temporal logic
CMSB'07 Proceedings of the 2007 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
Proceedings of the 9th International Conference on Computational Methods in Systems Biology
A Pattern Mining Approach for Classifying Multivariate Temporal Data
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Snoopy --- a unifying petri net tool
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
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Systems biology aims to facilitate the understanding of complex interactions between components in biological systems. Petri nets (PN), and in particular Coloured Petri Nets (CPN) have been demonstrated to be a suitable formalism for modelling biological systems and building computational models over multiple spatial and temporal scales. To explore the complex and high-dimensional solution space over the behaviours generated by such models, we propose a clustering methodology which combines principal component analysis (PCA), distance similarity and density factors through the application of DBScan. To facilitate the interpretation of clustering results and enable further analysis using model checking we apply a pattern mining approach aimed at generating high-level classificatory descriptions of the clusters' behaviour in temporal logic. We illustrate the power of our approach through the analysis of two case studies: multiple knockdown of the Mitogen-activated protein-kinase (MAPK) pathway, and selective knockout of Planar Cell Polarity (PCP) signalling in Drosophila wing.