Abstract

Introduction Powerful new technologies, such as DNA microarrays, provide simple and economical ways to explore gene expression patterns on a genomic scale 1,2 . Using observed gene expression data, recent advances of technology in bioinformatics have made gene expression comprehensive and several approaches have been proposed to infer the genetic networks 3-7 . We previously introduced two top-down approaches for the inference of interrelated mechanism among genes in genetic network that is based on the steady state and temporal analyses of gene expression patterns against some kinds of gene perturbations such as disruption or over expression. The former analysis is performed by a static Boolean network model based on a multi-level digraph approach 8 that can treat a large number of expression data. The latter one is by a dynamic network model such as S-system 9 that can infer the interrelated mechanism in genetic netwo

Citations