Abstract: Causality analysis of rule-based models allows the reconstruction of the causal paths leading to chosen events of interest. This potentially reveals emerging paths that were completely unknown at the time of creation of a model. However, current implementations provide results in the form of a collection of stories. For large models, this can amount to hundreds of story graphs to read and interpret for a single event of interest. In this work, we hence develop a method to fold a collection of stories into a single quotient graph. The main challenge is to find a trade-off in the partitioning of story events which will maximize compactness without loosing important details about information propagation in the model. The partitioning criterion proposed is relevant context, the context from an event's past which remains useful in its future. Each step of the method is illustrated on a toy rule-based model. This work is part of a longer term objective to automatically extract biological pathways from rule-based models.

@inProceedings{legare-JOBIN21,
   author =    {S{\'e}bastien L{\'e}gar{\'e} and Jean Krivine and  J{\'e}r{\^o}me Feret},
   title =     {Distinguishing Context Dependent Events in Quotients of Causal Stories},
   editor =    {Sophie Schbath and Denis Thieffry },
   booktitle = {Proceedings of JOBIM 2021 - Journ\'ees Ouvertes en Biologie, Informatique et Math\'ematiques. },
   address =   {Institut Pasteur (conférence virtuelle), France},
   url = {https://jobim2021.sciencesconf.org/data/proceedings_talks_jobim2021.pdf}
   pages =     {54--61},
   month =     {06--09 July},
   year =      {2021},
}