  Gene regulation is a series of processes that control gene expression and its
extent. The connections among genes and their regulatory molecules, usually
transcription factors, and a descriptive model of such connections, are known
as gene regulatory networks (GRNs). Elucidating 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 gene regulatory
networks. 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 propose new MapReduce algorithms for inferring gene regulatory networks
on a Hadoop cluster in a cloud environment. These algorithms employ an
information-theoretic approach to infer GRNs using time-series microarray data.
Experimental results show that our MapReduce program is much faster than an
existing tool while achieving slightly better prediction accuracy than the
existing tool.
