Boolean Network Identification from Multiplex Time Series Data

Abstract : Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logical models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scal-able training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that goal, we exhibit a necessary condition that must be satisfied by a Boolean network dynamics to be consistent with a discretized time series trace. Based on this condition, we use a declarative programming approach (Answer Set Programming) to compute an over-approximation of the set of Boolean networks which fit best with experimental data. Combined with model-checking approaches, we end up with a global learning algorithm and compare it to learning approaches based on static data.
Type de document :
Communication dans un congrès
Olivier Roux; Jérémie Bourdon. CMSB 2015 - 13th conference on Computational Methods for Systems Biology, Sep 2015, Nantes, France. Springer International Publishing, 9308, pp.170-181, Lecture Notes in Computer Science. 〈http://cmsb2015.sciencesconf.org/〉. 〈10.1007/978-3-319-23401-4_15〉
Liste complète des métadonnées

Littérature citée [15 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01164751
Contributeur : Loïc Paulevé <>
Soumis le : mercredi 17 juin 2015 - 17:02:38
Dernière modification le : mercredi 19 décembre 2018 - 15:02:04
Document(s) archivé(s) le : mardi 25 avril 2017 - 11:11:23

Fichier

report.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Max Ostrowski, Loïc Paulevé, Torsten Schaub, Anne Siegel, Carito Guziolowski. Boolean Network Identification from Multiplex Time Series Data. Olivier Roux; Jérémie Bourdon. CMSB 2015 - 13th conference on Computational Methods for Systems Biology, Sep 2015, Nantes, France. Springer International Publishing, 9308, pp.170-181, Lecture Notes in Computer Science. 〈http://cmsb2015.sciencesconf.org/〉. 〈10.1007/978-3-319-23401-4_15〉. 〈hal-01164751〉

Partager

Métriques

Consultations de la notice

992

Téléchargements de fichiers

396