Function approximation approach to the inference of reduced NGnet models of genetic networks

Background: The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of models have been proposed to describe genetic regulatory networks, this study focuses on a set of differential equations since it has the ability to model dynamic behavior of gene expression. When we use a set of differential equations to describe genetic networks, the inference problem can be defined as a function approximation problem. On the basis of this problem definition, we propose in this study a new method to infer reduced NGnet models of genetic networks. Results: Through numerical experiments on artificial genetic network inference problems, we demonstrated that our method has the ability to infer genetic networks correctly and it was faster than the other inference methods. We then applied the proposed method to actual expression data of the bacterial SOS DNA repair system, and succeeded in finding...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Shuhei Kimura, Katsuki Sonoda, Soichiro Yamane, Hideki Maeda, Koki Matsumura, Mariko Hatakeyama
• Keywords: BMCBI 2008, Gene Expression, Genetic Networks, Inference