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Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

BACKGROUND: Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and...

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Autores principales: Bailly-Bechet, Marc, Braunstein, Alfredo, Pagnani, Andrea, Weigt, Martin, Zecchina, Riccardo
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2909222/
https://www.ncbi.nlm.nih.gov/pubmed/20587029
http://dx.doi.org/10.1186/1471-2105-11-355
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author Bailly-Bechet, Marc
Braunstein, Alfredo
Pagnani, Andrea
Weigt, Martin
Zecchina, Riccardo
author_facet Bailly-Bechet, Marc
Braunstein, Alfredo
Pagnani, Andrea
Weigt, Martin
Zecchina, Riccardo
author_sort Bailly-Bechet, Marc
collection PubMed
description BACKGROUND: Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels. RESULTS: We propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network. CONCLUSIONS: The algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results [1]. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases.
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spelling pubmed-29092222010-07-24 Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach Bailly-Bechet, Marc Braunstein, Alfredo Pagnani, Andrea Weigt, Martin Zecchina, Riccardo BMC Bioinformatics Methodology Article BACKGROUND: Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels. RESULTS: We propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network. CONCLUSIONS: The algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results [1]. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases. BioMed Central 2010-06-29 /pmc/articles/PMC2909222/ /pubmed/20587029 http://dx.doi.org/10.1186/1471-2105-11-355 Text en Copyright ©2010 Bailly-Bechet et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Bailly-Bechet, Marc
Braunstein, Alfredo
Pagnani, Andrea
Weigt, Martin
Zecchina, Riccardo
Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
title Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
title_full Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
title_fullStr Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
title_full_unstemmed Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
title_short Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
title_sort inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2909222/
https://www.ncbi.nlm.nih.gov/pubmed/20587029
http://dx.doi.org/10.1186/1471-2105-11-355
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