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Information processing in the transcriptional regulatory network of yeast: Functional robustness
BACKGROUND: Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2679710/ https://www.ncbi.nlm.nih.gov/pubmed/19298671 http://dx.doi.org/10.1186/1752-0509-3-35 |
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author | Emmert-Streib, Frank Dehmer, Matthias |
author_facet | Emmert-Streib, Frank Dehmer, Matthias |
author_sort | Emmert-Streib, Frank |
collection | PubMed |
description | BACKGROUND: Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism. RESULTS: In this paper we study the functional robustness of the transcriptional regulatory network of S. cerevisiae. We model the information processing in the network as a first order Markov chain and study the influence of single gene perturbations on the global, asymptotic communication among genes. Modification in the communication is measured by an information theoretic measure allowing to predict genes that are 'fragile' with respect to single gene knockouts. Our results demonstrate that the predicted set of fragile genes contains a statistically significant enrichment of so called essential genes that are experimentally found to be necessary to ensure vital yeast. Further, a structural analysis of the transcriptional regulatory network reveals that there are significant differences between fragile genes, hub genes and genes with a high betweenness centrality value. CONCLUSION: Our study does not only demonstrate that a combination of graph theoretical, information theoretical and statistical methods leads to meaningful biological results but also that such methods allow to study information processing in gene networks instead of just their structural properties. |
format | Text |
id | pubmed-2679710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-26797102009-05-09 Information processing in the transcriptional regulatory network of yeast: Functional robustness Emmert-Streib, Frank Dehmer, Matthias BMC Syst Biol Research Article BACKGROUND: Gene networks are considered to represent various aspects of molecular biological systems meaningfully because they naturally provide a systems perspective of molecular interactions. In this respect, the functional understanding of the transcriptional regulatory network is considered as key to elucidate the functional organization of an organism. RESULTS: In this paper we study the functional robustness of the transcriptional regulatory network of S. cerevisiae. We model the information processing in the network as a first order Markov chain and study the influence of single gene perturbations on the global, asymptotic communication among genes. Modification in the communication is measured by an information theoretic measure allowing to predict genes that are 'fragile' with respect to single gene knockouts. Our results demonstrate that the predicted set of fragile genes contains a statistically significant enrichment of so called essential genes that are experimentally found to be necessary to ensure vital yeast. Further, a structural analysis of the transcriptional regulatory network reveals that there are significant differences between fragile genes, hub genes and genes with a high betweenness centrality value. CONCLUSION: Our study does not only demonstrate that a combination of graph theoretical, information theoretical and statistical methods leads to meaningful biological results but also that such methods allow to study information processing in gene networks instead of just their structural properties. BioMed Central 2009-03-19 /pmc/articles/PMC2679710/ /pubmed/19298671 http://dx.doi.org/10.1186/1752-0509-3-35 Text en Copyright © 2009 Emmert-Streib and Dehmer; 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 | Research Article Emmert-Streib, Frank Dehmer, Matthias Information processing in the transcriptional regulatory network of yeast: Functional robustness |
title | Information processing in the transcriptional regulatory network of yeast: Functional robustness |
title_full | Information processing in the transcriptional regulatory network of yeast: Functional robustness |
title_fullStr | Information processing in the transcriptional regulatory network of yeast: Functional robustness |
title_full_unstemmed | Information processing in the transcriptional regulatory network of yeast: Functional robustness |
title_short | Information processing in the transcriptional regulatory network of yeast: Functional robustness |
title_sort | information processing in the transcriptional regulatory network of yeast: functional robustness |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2679710/ https://www.ncbi.nlm.nih.gov/pubmed/19298671 http://dx.doi.org/10.1186/1752-0509-3-35 |
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