A Time-Delayed Information-Theoretic Approach to the Reverse Engineering of Gene Regulatory Networks Using Apache Spark

Abstract

Elucidating gene regulatory networks (GRNs) is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer or reconstruct gene regulatory networks from expression data. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we present a new algorithm for reverse engineering (inferring) gene regulatory networks on a computer cluster in a cloud environment. The algorithm, implemented in Apache Spark, employs an information-theoretic approach to infer GRNs from time-series gene expression data. Experimental results show that our Spark program is much faster than an existing tool while achieving the same prediction accuracy.