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Kappa platform
Pierre Boutillier, Ferdinanda Camporesi, Jean Coquet, Jérôme Feret, Kim Quyên Lý, Nathalie Theret, & Pierre Vignet.
KaSa: A Static Analyzer for Kappa.
In Proceedings of the 16th Conference on Computational Methods in Systems Biology (CMSB 2018), Brno, Czech Republic, September 12--14, 2018. M. Ceska & D. Safranek (Eds.). In Lecture Notes in Computer Sciences / Lecture Notes in BioInformatics, volume 11095. © 2018, Springer.

Abstract: KaSa is a static analyzer for Kappa models. Its goal is two-fold. Firstly, KaSa assists the modeler by warning about potential issues in the model. Secondly, KaSa may provide useful properties to check that what is implemented is what the modeler has in mind and to provide a quick overview of the model for the people who have not written it.
The cornerstone of KaSa is a fix-point engine which detects some patterns that may never occur whatever the evolution of the system may be. From this, many useful information may be collected: KaSa warns about rules that may never be applied, about potential irreversible transformations of proteins (that may not be reverted even thanks to an arbitrary number of computation steps) and about the potential formation of unbounded molecular compounds. Lastly, KaSa detects potential influences (activation/inhibition relation) between rules.
In this paper, we illustrate the main features of KaSa on a model of the extracellular activation of the transforming growth factor, TGF-b.

  author    = {Pierre Boutillier and
               Ferdinanda Camporesi and
               Jean Coquet and
               J{\'{e}}r{\^{o}}me Feret and
               Kim Quy{\^{e}}n L{\'{y}} and
               Nathalie Th{\'{e}}ret and
               Pierre Vignet},
  title     = {KaSa: {A} Static Analyzer for Kappa},
  booktitle = {Computational Methods in Systems Biology - 16th International Conference,
               {CMSB} 2018, Brno, Czech Republic, September 12-14, 2018, Proceedings},
  editor    = {Milan Ceska and
               David Safr{\'{a}}nek},
  series    = {Lecture Notes in Computer Science},
  volume    = {11095},
  publisher = {Springer},
  year      = {2018},
  url       = {https://doi.org/10.1007/978-3-319-99429-1},
  doi       = {10.1007/978-3-319-99429-1},
  isbn      = {978-3-319-99428-4},
  pages     = {285--291},
  year      = {2018},
  doi       = {10.1007/978-3-319-99429-1\_17}}