Koji M. Kyoda[1,2](kyoda@symbio.jst.go.jp)
Mineo Morohashi[1,3](moro@symbio.jst.go.jp)
Shuichi Onami[1,4](sonami@symbio.jst.go.jp)
Hiroaki Kitano[1,4,5](kitano@symbio.jst.go.jp)
[1]Kitano Symbiotic Systems Project, ERATO, JST., M31 6A, 6-31-15 Jingumae,
Shibuya-ku, Tokyo 150-0001, Japan
[2]School of Fundamental Science and Technology, Keio University,
[3]Medical Research Institute, Tokyo Medical and Dental University,
[4]Department of Control and Dynamical Systems 107-81, California Institute of Technology,
1200 East California BoulevardPasadena, CA 91125, USA
[5]Sony Computer Science Laboratories, Inc., 3-14-13 Higashi-Gotanda, Shinagawa-ku,
Tokyo 141-0022, Japan
In this paper we introduce a new inference method of a gene regulatory network from steady-state gene expression data. Our method determines a regulatory structure consistent with an observed set of steady-state expression profiles, each generated from wild-type and single deletion mutant of the target network. Our method derives the regulatory relationships in the network using a graph theoretic approach. The advantage of our method is to be able to deal with continuous values of steady-state data, while most of the methods proposed in past use a Boolean network model with binary data. Performance of our method is evaluated on simulated networks with varying the size of networks, indegree of each gene, and the data characteristics (continuous-value/binary), and is compared with that of predictor method proposed by Ideker et al. As a result, we show the superiority of using continuous values to binary values, and the performance of our method is much better than that of the predictor method.