Integrated System for Inference of Gene Expression Network

Recent advances of powerful new technologies 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 networks by using these observed data. We previously proposed an efficient numerical optimization technique by using time-course data of system components, which is based on real-coded genetic algorithm (RCGAs [1, 2]) to estimate the interrelated coefficients among system components of a dynamic network model called S-system [3] that is a type of power-low formalism and is suitable for description of organizationally complex systems such as gene expression networks and metabolic pathways. This technique with the combination of unimodal normal distribution crossover (UNDX) [1], which is one of the crossover operators for RCGAs, with the alternation of generation model called minimal generation gap (MGG) [2] showed superiority to the conventional simple genetic algorithm [4, 5]. In this study we shall describe on the integrated inferring system involving the GUI program for real-time visualization of the inferred network structures by using distributed parallel computer systems.