Abstract
Recent advances of powerful new technology such as DNA microarrays provide a mass of gene expression data on a genomic scale. One of the most important projects in post-genome-era is the system identification of gene expression network by using these observed data.
We previously proposed an efficient numerical optimization technique by using time-course data of system components, which based on real-coded genetic algorithms (RCGAs) to estimate the interrelated coefficients among system components of a dynamic network model called S-system (Savageau, 1976) that is a type of power-law formalism and is suitable for description of organizationally complex systems such as gene expression networks and metabolic pathways.
It is inverse problem to infer internal structure of gene expressions from experimentally observed time-course data. So, we get a lot of gene network candidates, which can explain experimentally observed facts. In this study we shall describe on the integrated inferring system involving the GUI program for real time visualization for the inferred network structures by using distributed parallel computer system. And we developed a module that extracts common core interactions from many kinds of network candidates. We also calculate and compare sensitivity of each interaction contained in the inferred gene networks. In the results, common core interactions include essential and rigid interactions that possibly explain the observed time-course data.
