Abstract
The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes - as is the case in biological networks - due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.
Highlights
Microarray analysis methods produce large sets of gene expression data that can be exploited for novel insights into the fundamentals of molecular biology research
Since the structure to infer is a gene network, graphical models have been proposed and developed. This is the case of BANJO [2], that assumes that the gene network can be modeled as a Bayesian network
In section Methods we describe the Network Identification by multiple Regression (NIR) algorithm and its parallel implementation; in section Results we describe experimental results and in section Discussion we give our conclusions
Summary
Microarray analysis methods produce large sets of gene expression data that can be exploited for novel insights into the fundamentals of molecular biology research. Gene network inference algorithms based on ODEs relate gene transcript concentration changes to each other and to an external perturbation. NIR algorithm achieves better results than the other network inference algorithms being able to reach peaks of 95% of correct predicted interactions This has been shown in [8] where there have been selected and compared the algorithms capable of solving the network inference problem with an available ready-touse software. In that work it wasn’t possible to run NIR on data set with more than 100 genes because it would have taken too much time. In section Methods we describe the NIR algorithm and its parallel implementation; in section Results we describe experimental results and in section Discussion we give our conclusions
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