Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.

Through many researches on modeling and analyzing biological pathways, Petri net has recognized as a promising method for representing biological pathways. Recently, Matsuno et al. (2003) introduced hybrid functional Petri net (HFPN) for giving more intuitive and natural biological pathway modeling method than existing Petri nets. They also developed Genomic Object Net (GON) which employs the HFPN as a basic architecture. Many kinds of biological pathways have been modeled with the HFPN and simulated by the GON. This paper gives a new HFPN model of "cell cycle of fission yeast" with giving six basic HFPN components of typical biological reactions, and demonstrating the method how biological pathways can be modeled with these HFPN components. Simulation results by GON suggest a new hypothesis which will help biologist for performing further experiments.