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Network Features and Pathway Analyses of a Signal Transduction Cascade
The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive protei...
Autores principales: | , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Frontiers Research Foundation
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2699032/ https://www.ncbi.nlm.nih.gov/pubmed/19543432 http://dx.doi.org/10.3389/neuro.11/013.2009 |
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author | Yanashima, Ryoji Kitagawa, Noriyuki Matsubara, Yoshiya Weatheritt, Robert Oka, Kotaro Kikuchi, Shinichi Tomita, Masaru Ishizaki, Shun |
author_facet | Yanashima, Ryoji Kitagawa, Noriyuki Matsubara, Yoshiya Weatheritt, Robert Oka, Kotaro Kikuchi, Shinichi Tomita, Masaru Ishizaki, Shun |
author_sort | Yanashima, Ryoji |
collection | PubMed |
description | The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path. |
format | Text |
id | pubmed-2699032 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Frontiers Research Foundation |
record_format | MEDLINE/PubMed |
spelling | pubmed-26990322009-06-19 Network Features and Pathway Analyses of a Signal Transduction Cascade Yanashima, Ryoji Kitagawa, Noriyuki Matsubara, Yoshiya Weatheritt, Robert Oka, Kotaro Kikuchi, Shinichi Tomita, Masaru Ishizaki, Shun Front Neuroinformatics Neuroscience The scale-free and small-world network models reflect the functional units of networks. However, when we investigated the network properties of a signaling pathway using these models, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. We analyzed signaling networks by focusing on those pathways that best reflected cellular function. Therefore, our analysis of pathways started from the ligands and progressed to transcription factors and cytoskeletal proteins. We employed the Python module to assess the target network. This involved comparing the original and restricted signaling cascades as a directed graph using microarray gene expression profiles of late onset Alzheimer's disease. The most commonly used method of shortest-path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We therefore introduced included k-shortest paths and k-cycles in our network analysis using the Python modules, which allowed us to attain a reasonable computational time and identify k-shortest paths. This technique reflected results found in vivo and identified pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the feedback loop and feedforward loop control structures as an alternative path. Frontiers Research Foundation 2009-05-29 /pmc/articles/PMC2699032/ /pubmed/19543432 http://dx.doi.org/10.3389/neuro.11/013.2009 Text en Copyright © 2009 Yanashima, Kitagawa, Matsubara, Weatheritt, Oka, Kikuchi, Tomita and Ishizaki. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited |
spellingShingle | Neuroscience Yanashima, Ryoji Kitagawa, Noriyuki Matsubara, Yoshiya Weatheritt, Robert Oka, Kotaro Kikuchi, Shinichi Tomita, Masaru Ishizaki, Shun Network Features and Pathway Analyses of a Signal Transduction Cascade |
title | Network Features and Pathway Analyses of a Signal Transduction Cascade |
title_full | Network Features and Pathway Analyses of a Signal Transduction Cascade |
title_fullStr | Network Features and Pathway Analyses of a Signal Transduction Cascade |
title_full_unstemmed | Network Features and Pathway Analyses of a Signal Transduction Cascade |
title_short | Network Features and Pathway Analyses of a Signal Transduction Cascade |
title_sort | network features and pathway analyses of a signal transduction cascade |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2699032/ https://www.ncbi.nlm.nih.gov/pubmed/19543432 http://dx.doi.org/10.3389/neuro.11/013.2009 |
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