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Uncovering signal transduction networks from high-throughput data by integer linear programming
Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understa...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2396433/ https://www.ncbi.nlm.nih.gov/pubmed/18411207 http://dx.doi.org/10.1093/nar/gkn145 |
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author | Zhao, Xing-Ming Wang, Rui-Sheng Chen, Luonan Aihara, Kazuyuki |
author_facet | Zhao, Xing-Ming Wang, Rui-Sheng Chen, Luonan Aihara, Kazuyuki |
author_sort | Zhao, Xing-Ming |
collection | PubMed |
description | Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system. |
format | Text |
id | pubmed-2396433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-23964332008-05-28 Uncovering signal transduction networks from high-throughput data by integer linear programming Zhao, Xing-Ming Wang, Rui-Sheng Chen, Luonan Aihara, Kazuyuki Nucleic Acids Res Methods Online Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system. Oxford University Press 2008-05 2008-04-13 /pmc/articles/PMC2396433/ /pubmed/18411207 http://dx.doi.org/10.1093/nar/gkn145 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methods Online Zhao, Xing-Ming Wang, Rui-Sheng Chen, Luonan Aihara, Kazuyuki Uncovering signal transduction networks from high-throughput data by integer linear programming |
title | Uncovering signal transduction networks from high-throughput data by integer linear programming |
title_full | Uncovering signal transduction networks from high-throughput data by integer linear programming |
title_fullStr | Uncovering signal transduction networks from high-throughput data by integer linear programming |
title_full_unstemmed | Uncovering signal transduction networks from high-throughput data by integer linear programming |
title_short | Uncovering signal transduction networks from high-throughput data by integer linear programming |
title_sort | uncovering signal transduction networks from high-throughput data by integer linear programming |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2396433/ https://www.ncbi.nlm.nih.gov/pubmed/18411207 http://dx.doi.org/10.1093/nar/gkn145 |
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