A Parallel Solution to Infer Genetic Network Architectures in Gene Expression Analysis

Abstract

With the recent DNA-microarray technology, it is possible to measure the expression levels of thousands of genes simultaneously in the same experiment. A genetic network is a model that describes how the expression level of each gene is affected by the expression levels of other genes in the network. In this paper we explore the use of parallel computers to infer genetic network architectures in gene expression analysis. Given the results of an experiment with n genes and m measures over time ( m [UNKNOWN] n), we consider the problem of finding a subset of genes ( k genes, where k [UNKNOWN] n) that explain the expression level of a given target gene under study. We consider the coarse-grained multicomputer (CGM) model, with p processors. We first present a sequential approximation algorithm of O( m4 n) time and O( m2 n) space. The main result is a new parallel approximation algorithm that determines the k genes in O( m4 n/p) local computing time plus O( k) communication rounds, and with space requirement of O( m2 n/p). The p factor in the parallel time and space complexities indicates a good parallelization. To our knowledge there are no CGM algorithms for the problem considered in this paper. We also show promising experimental results on a Beowulf machine. As will be shown in our experiments, we observe very promising speedups results, especially in the cases where the number of genes studied exceeds 4000. Notice that even with current microarray technology, microchips with around 15,000 spots are already possible. The proposed parallel method constitutes thus an excellent example of application of high-performance computing in this important field.