An Algebraic Approach for Inferring and Using Symmetries in Rule-based Models - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

An Algebraic Approach for Inferring and Using Symmetries in Rule-based Models

Résumé

Symmetries arise naturally in rule-based models, and under various forms. Besides automorphisms between site graphs, which are usually built within the semantics, symmetries can take the form of pairs of sites having the same capabilities of interactions, of some protein variants behaving exactly the same way, or of some linear, planar, or 3D molecular complexes which could be seen modulo permutations of their axis and/or mirror-image symmetries. In this paper, we propose a unifying handling of symmetries in Kappa. We follow an algebraic approach, that is based on the single pushout semantics of Kappa. We model classes of symmetries as finite groups of transformations between site graphs, which are compatible with the notion of embedding (that is to say that it is always possible to restrict a symmetry that is applied with the image of an embedding to the domain of this embedding) and we provide some assumptions that ensure that symmetries are compatible with pushouts. Then, we characterise when a set of rules is symmetric with respect to a group of symmetries and, in such a case, we give sufficient conditions so that this group of symmetries induces a forward bisimulation and/or a backward bisimulation over the population semantics.
Fichier principal
Vignette du fichier
sasb2014.pdf (394.74 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01098556 , version 1 (19-10-2018)

Identifiants

Citer

Jérôme Feret. An Algebraic Approach for Inferring and Using Symmetries in Rule-based Models. Static Analysis and Systems Biology, Heinz Koeppl; Loïc Paulevé, Sep 2014, Munich, Germany. pp.20, ⟨10.1016/j.entcs.2015.06.010⟩. ⟨hal-01098556⟩
163 Consultations
104 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More