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Nucleic Acids Research Advance Access published online on April 13, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn145
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© 2008 The Author(s)
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.

Methods Online

Uncovering signal transduction networks from high-throughput data by integer linear programming

Xing-Ming Zhao1,2,3,4, Rui-Sheng Wang5, Luonan Chen1,3,4,5 and Kazuyuki Aihara1,3,*

1ERATO Aihara Complexity Modelling Project, JST, Tokyo 151-0064, Japan, 2Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Hefei, Anhui, China, 3Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan, 4Institute of Systems Biology, Shanghai University, China and 5Department of Electrical Engineering and Electronics, Osaka Sanyo University, Osaka 574-8530, Japan

*To whom correspondence should be addressed. Tel: +81 3 5452 6691; Fax: +81 3 5452 6692; Email: aihara{at}sat.t.u-tokyo.ac.jp

Received December 10, 2007. Revised February 19, 2008. Accepted March 14, 2008.

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.


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