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Bioinformatics Advance Access published online on October 28, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti071
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received June 16, 2004
Revised September 1, 2004
Accepted September 18, 2004

Article

Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm

Shuhei Kimura 1*, Kaori Ide 1, Aiko Kashihara 2, Makoto Kano 3, Mariko Hatakeyama 1, Ryoji Masui 4, Noriko Nakagawa 5, Shigeyuki Yokoyama 6, Seiki Kuramitsu 5, and Akihiko Konagaya 1

1 Bioinformatics Group, RIKEN Genomic Sciences Center, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan; Structurome Group, RIKEN Harima Institute at SPring-8, 1-1-1 Kohto, Mikazuki-cho, Sayo, Hyogo 679-5148, Japan
2 Structurome Group, RIKEN Harima Institute at SPring-8, 1-1-1 Kohto, Mikazuki-cho, Sayo, Hyogo 679-5148, Japan
3 Tokyo Research Laboratory, IBM Japan, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan
4 Structurome Group, RIKEN Harima Institute at SPring-8, 1-1-1 Kohto, Mikazuki-cho, Sayo, Hyogo 679-5148, Japan; Department of Biophysics and Biochemistry, Graduate School of Science, the University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
5 Structurome Group, RIKEN Harima Institute at SPring-8, 1-1-1 Kohto, Mikazuki-cho, Sayo, Hyogo 679-5148, Japan; Department of Biology, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan
6 Structurome Group, RIKEN Harima Institute at SPring-8, 1-1-1 Kohto, Mikazuki-cho, Sayo, Hyogo 679-5148, Japan; Department of Biophysics and Biochemistry, Graduate School of Science, the University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan; Protein Research Group, RIKEN Genomic Sciences Center, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan; Cellular Signaling Laboratory, RIKEN Harima Institute at SPring-8, 1-1-1 Kohto, Mikazuki-cho, Sayo, Hyogo 679-5148, Japan

* To whom correspondence should be addressed.
Shuhei Kimura, E-mail: skimura{at}gsc.riken.jp


  Abstract

Motivation: To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation.

Results: We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data.

Supplementary Information: See Bioinformatics Online.


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