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Original Research ARTICLE

This article is part of a Research Topic

Network features and pathway analyses of a signal transduction cascade

Ryoji Yanashima^1,2, Noriyuki Kitagawa^1,2, Yoshiya Matsubara^1,2, Robert Weatheritt³, Kotaro Oka⁴, Shinichi Kikuchi^1,5*, Masaru Tomita^1,5 and Shun Ishizaki⁵

Institute for Advanced Biosciences, Keio University, Japan

Graduate School of Media and Governance, Keio University, Japan

Department of Biology, Chemistry and Computer Science, University of York, UK

Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Japan

Faculty of Environment and Information Studies, Keio University, Japan

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.

Keywords:

signal transduction, Alzheimer’s disease, network analysis, k-shortest path analysis, python, network robustness, graph theory, hippocampal CA1

Citation:

Yanashima R, Kitagawa N, Matsubara Y, Weatheritt R, Oka K, Kikuchi S, Tomita M and Ishizaki S (2009). Network features and pathway analyses of a signal transduction cascade. Front. Neuroinform. 3:13. doi:10.3389/neuro.11.013.2009

Received:

14 September 2008;

Paper pending published:

30 September 2008;

Accepted:

30 April 2009;

Published online:

29 May 2009.

Edited by:

Rolf Kötter, Radboud University Nijmegen, The Netherlands

Reviewed by:

Marcus Kaiser, Newcastle University, UK
Bruce Southey, University of Illinois, USA

© 2009 Yanashima, Kitagawa, Matsubara, Weatheritt, Oka, Kikuchi, Tomita and Ishizaki. 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.

*Correspondence:

Shinichi Kikuchi, Institute for Advanced Biosciences and Faculty of Environment and Information Studies, Keio University, Endo 5322, Fujisawa 252-8520, Japan. e-mail: kikuchi@sfc.keio.ac.jp