Function approximation approach to the inference of 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.With recent advances in technologies such as DNA microarrays, it has become possible to measure gene expression patterns on a genomic scale. One expected use of these data is to predict functions of genes through the inference of regulatory interactions of genes, i.e., a genetic network. There are increasing needs to reveal unknown functions of genes. Therefore, many researchers have become interested in the inference of genetic networks, and the development of this methodology has become a major topic in the bioinformatics field.Numerous models to describe genetic networks have been proposed [1-10]. This study however focuses especially on a set of differential equations since it has an ability to capture dynamic behavior of gene expression. In the genetic network inference problem based on the set of differential equations, a genetic network is described aswhere Xn is the expression level of the n-th g