System biology problems such as whole-genome network construction from large-scale
gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms
are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively
parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing
gene regulatory networks from large-scale datasets have drawn the noticeable attention of
researchers in the field of parallel computing and system biology. In this paper, we attempt to
provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory
networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges
are given. Secondly, a detailed description of the four parallel frameworks and libraries including
CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed.
Finally, some conclusions and guidelines for parallel reverse engineering are described.