Abstracting the dynamics of biological pathways using information theory: a case study of apoptosis pathway

Abstract : Motivation: Quantitative models are increasingly used in systems biology. Usually, these quantitative models involve many molecular species and their associated reactions. When simulating a tissue with thousands of cells, using these large models becomes computationally and time limiting. Results: In this paper, we propose to construct abstractions using information theory notions. Entropy is used to discretize the state space and mutual information is used to select a subset of all original variables and their mutual dependencies. We apply our method to an hybrid model of TRAIL-induced apoptosis in HeLa cell. Our abstraction, represented as a Dynamic Bayesian Network (DBN), reduces the number of variables from 92 to 10, and accelerates numerical simulation by an order of magnitude, yet preserving essential features of cell death time distributions.
Keywords : Systems biology
Type de document :
Article dans une revue
Bioinformatics, Oxford University Press (OUP), 2017, 33 (13), pp.1980 - 1986. 〈10.1093/bioinformatics/btx095〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01547618
Contributeur : Gregory Batt <>
Soumis le : mardi 27 juin 2017 - 01:09:22
Dernière modification le : mercredi 11 avril 2018 - 01:51:25

Identifiants

Citation

Sucheendra K. Palaniappan, François Bertaux, Matthieu Pichené, Eric Fabre, Gregory Batt, et al.. Abstracting the dynamics of biological pathways using information theory: a case study of apoptosis pathway. Bioinformatics, Oxford University Press (OUP), 2017, 33 (13), pp.1980 - 1986. 〈10.1093/bioinformatics/btx095〉. 〈hal-01547618〉

Partager

Métriques

Consultations de la notice

276