Prediction of Debacle Points for Robustness of Biological Pathways by Using Recurrent Neural Networks

Hironori Kitakaze[1],[2] (
Hiroshi Matsuno[3] (
Nobuhiko Ikeda[4] (
Satoru Miyano[5] (

[1]Oshima College of Maritime Technology, 1091-1 Oshima-cho, Oshima-gun, Yamaguchi 742-2193, Japan
[2]Graduate School of Science and Engineering, Yamaguchi University, 1677-1 Yoshida, Yamaguchi-shi, Yamaguchi 753-8512, Japan
[3]Faculty of Science, Yamaguchi University, 1677-1 Yoshida, Yamaguchi-shi, Yamaguchi 753-8512, Japan
[4]Tokuyama College of Technology, 3538 Takajo, Kume, Shunan-shi, Yamaguchi 745-8585, Japan
[5]Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan


Living organisms have ingenious control mechanisms in which many molecular interactions work for keeping their normal activities against disturbances inside and outside of them. However, at the same time, the control mechanism has debacle points at which the stability can be broken easily. This paper proposes a new method which uses recurrent neural network for predicting debacle points in an hybrid functional Petri net model of a biological pathway. Evaluation on an apoptosis signaling pathway indicates that the rates of 96.5% of debacle points and 65.5% of non-debacle points can be predicted by the proposed method.

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Japanese Society for Bioinformatics