Function approximation approach to the inference of reduced NGnet models of genetic networks
1 Faculty of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori, Japan
2 JFE R&D Corporation, 1-1 Minami-Watarida, Kawasaki, Japan
3 JFE Engineering Corporation, 2-1 Suehiro, Tsurumi, Yokohama, Japan
4 RIKEN Genomic Sciences Center, 1-7-22 Suehiro, Tsurumi, Yokohama, Japan
BMC Bioinformatics 2008, 9:23 doi:10.1186/1471-2105-9-23Published: 14 January 2008
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.
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 several reasonable regulations. When our method inferred the genetic network from the actual data, it required about 4.7 min on a single-CPU personal computer.
The proposed method has an ability to obtain reasonable networks with a short computational time. As a high performance computer is not always available at every laboratory, the short computational time of our method is a preferable feature. There does not seem to be a perfect model for the inference of genetic networks yet. Therefore, in order to extract reliable information from the observed gene expression data, we should infer genetic networks using multiple inference methods based on different models. Our approach could be used as one of the promising inference methods.