Modeling stochasticity and robustness in gene regulatory networks.

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Ressource 1Télécharger: BIB_3C1AE80B8836.P001.pdf (973.89 [Ko])
Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_3C1AE80B8836
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Modeling stochasticity and robustness in gene regulatory networks.
Périodique
Bioinformatics
Auteur(s)
Garg A., Mohanram K., Di Cara A., De Micheli G., Xenarios I.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Statut éditorial
Publié
Date de publication
2009
Volume
25
Numéro
12
Pages
i101-i109
Langue
anglais
Résumé
MOTIVATION: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations.
RESULTS: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs.
AVAILABILITY: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.
Mots-clé
Algorithms, Computational Biology/methods, Gene Expression Profiling/methods, Gene Regulatory Networks
Pubmed
Web of science
Open Access
Oui
Création de la notice
17/10/2012 12:03
Dernière modification de la notice
20/08/2019 13:32
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