Graphs, Rewriting and Pathway Reconstruction for Rule-Based Models

Vincent Danos, Jerome Feret, Walter Fontana, Russell Harmer, Jonathan Hayman, Jean Krivine, Chris Thompson-Walsh, Glynn Winskel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this paper, we introduce a novel way of constructing concise causal histories (pathways) to represent how specified structures are formed during simulation of systems represented by rule-based models. This is founded on a new, clean, graph-based semantics introduced in the first part of this paper for Kappa, a rule-based modelling language that has emerged as a natural description of protein-protein interactions in molecular biology [Bachman 2011]. The semantics is capable of capturing the whole of Kappa, including subtle side-effects on deletion of structure, and its structured presentation provides the basis for the translation of techniques to other models. In particular, we give a notion of trajectory compression, which restricts a trace culminating in the production of a given structure to the actions necessary for the structure to occur. This is central to the reconstruction of biochemical pathways due to the failure of traditional techniques to provide adequately concise causal histories, and we expect it to be applicable in a range of other modelling situations.
Original languageEnglish
Title of host publicationIARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2012)
EditorsDeepak D'Souza, Telikepalli Kavitha, Jaikumar Radhakrishnan
Place of PublicationDagstuhl, Germany
PublisherSchloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany
Number of pages13
Publication statusPublished - 2012

Publication series

NameLeibniz International Proceedings in Informatics (LIPIcs)
PublisherSchloss Dagstuhl--Leibniz-Zentrum fuer Informatik

Keywords / Materials (for Non-textual outputs)

  • concurrency
  • rule-based models
  • graph rewriting
  • pathways
  • causality


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