CURATING A LARGE-SCALE REGULATORY NETWORK BY EVALUATING ITS CONSISTENCY WITH EXPRESSION DATASETS

Carito Guziolowski 1, * Jérémy Gruel 2 Ovidiu Radulescu 3 Anne Siegel 1
* Corresponding author
1 SYMBIOSE - Biological systems and models, bioinformatics and sequences
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The analysis of large-scale regulatory models using data issued from genome-scale highthroughput experimental techniques is an actual challenge in the systems biology field. This kind of analysis faces three common problems: the size of the model, the uncertainty in the expression datasets, and the heterogeneity of the data. On that account, we propose a method that analyses large-scale networks with small - but reliable - expression datasets. Our method relates regulatory knowledge with heterogeneous expression datasets using a simple consistency rule. If a global consistency is found, we predict the changes in gene expression or protein activity of some components of the network. When the whole model is inconsistent, we highlight regions in the network where the regulatory knowledge is incomplete. Confronting our predictions with mRNA expression experiments allows us to determine the missing post-transcriptional interactions of our model. We tested this approach with the transcriptional network of E. coli. Sources and a working application of our method can be accessed on-line at: http://www.irisa.fr/symbiose/bioquali/
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Carito Guziolowski, Jérémy Gruel, Ovidiu Radulescu, Anne Siegel. CURATING A LARGE-SCALE REGULATORY NETWORK BY EVALUATING ITS CONSISTENCY WITH EXPRESSION DATASETS. CIBB 2008: COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, Oct 2008, Salerno, Italy. pp.144-155. ⟨inria-00331385⟩

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