Comparative Analysis of Gene Regulatory Networks

The advent of sequencing technologies has increased accessibility to large scale gene expression data. As a result, biology has become a more mathematically and computationally heavy field than ever before in order to efficiently process and analyze the data available. These large datasets allow researchers to ask comprehensive questions they could not before about gene expression at the systems level. The novelty of these technologies, however, is such that there is not yet a set standard for the modeling of biological systems. Previous studies that use gene regulatory networks in Maize to study regulatory molecules focus on the study of transcription factors (TFs) but rarely focus on other regulatory molecules. The understanding of other regulators like long non-coding RNAs’ (ncRNAs) regulatory functions are comparatively limited. Hench through the use of large datasets processed by certain algorithms, researchers can learn much about the pathways lncRNAs are involved in but have yet to be understood. The ensemble forest method employed by the gene regulatory network building algorithms iRafNet and GENIE3 make use of multiple randomized decision tree calculations to make the most likely possible regulatory molecule prediction for a gene. GENIE3 whose random forest method won the Dialogue for Reverse Engineering Assessments and Methods (DREAM) In Silico Multifactorial challenge was found to be less effective than the algorithm iRafNet under certain circumstances. iRafNet’s use of supplementary protein-protein interaction, time series, and knock out data allowed it to perform more favorably than GENIE3 according to the same criteria that the DREAM challenge used to rank GENIE3 the most effective algorithm of the challenge. The computational complexity of the algorithms is the same as is the language of implementation and type of input data. The effectiveness of each of these algorithms is mostly dependent on factors like data availability which for less studied regulatory molecules like lncRNA may be difficult. In the absences of the types of supplementary data iRafNet is capable of reading, there is no real advantage to its use.