Constructing Gene Regulatory Networks on Clusters of Cell Processors

Abstract

Constructing genome-wide gene regulatory networks from a large number of gene expression profile measurements is an important problem in systems biology. While several techniques have been developed, none of them is parallel, and they lack the capability to scale to the whole-genome level or incorporate the largest data sets, particularly with rigorous statistical testing. To address this problem, we recently developed a mutual information theory based parallel method for gene network reconstruction. In this paper, we extend this work to a cluster of Cell processors. We use parallelization across multiple Cells, multiple cores within each Cell, and vector units within the cores to develop a high performance implementation that effectively addresses the scaling problem. We present experimental results comparing the Cell implementation with a standard uniprocessor implementation and an implementation on a conventional supercomputer. Finally, we report the construction of a large 15,203 gene network of the plant Arabidopsis thaliana from 2,996 microarray experiments on a 8-node Cell blade cluster in 2 hours and 24 minutes.