'Gridifying' an Evolutionary Algorithm for Inference of Genetic Networks Using the Improved GOGA Framework and Its Performance Evaluation on OBI Grid

摘要

This paper presents a genetic algorithm running on a grid computing environment for inference of genetic networks. In bioinfor-matics, inference of genetic networks is one of the most important problems, in which mutual interactions among genes are estimated by using gene-expression time-course data. Network-Structure-Search Evolutionary Algorithm (NSS-EA) is a promising inference method of genetic networks that employs S-system as a model of genetic network and a genetic algorithm (GA) as a search engine. In this paper, we propose an implementation of NSS-EA running on a multi-PC-cluster grid computing environment where multiple PC clusters are connected over the Internet. We "Gridifiy" NSS-EA by using a framework for the development of GAs running on a multi-PC-cluster grid environment, named Grid-Oriented Genetic Algorithm Framework (GOGA Framework). We examined whether the "Gridified" NSS-EA works correctly and evaluated its performance on Open Bioinformatics Grid (OBIGrid) in Japan.