Abstract
Recent findings have elucidated that the regulation of messenger RNA (mRNA) levels is due to the synergistic and antagonist actions of transcription factors (TFs) and microRNAs (miRNAs). Mutual interactions among these molecules are easily modeled and analyzed using graphs whose nodes are molecules, and directed edges represent the associations among them. In particular, small subgraphs having three nodes also referred to as feed-forward loops (FFLs) or regulatory loops play a crucial role in many different diseases, such as cancer. Available technological platforms enable the investigation of only a single aspect of these mechanisms, e.g., the quantification of levels of mRNA or miRNA. Consequently, there exist different data sources for investigating some aspects of this problem, e.g., miRNA-mRNA or TF-mRNA associations. The comprehensive analysis is made possible only by the integration and the analysis of these data sources. Currently, the interest of researchers in this area is growing, the number of projects is increasing, and the number of challenges and issues for computer scientists is considerable. The need for an introductive survey from a computer science point of view consequently arises. This survey starts by discussing general concepts related to production of data. Then, main existing approaches of analysis are presented and discussed. Future improvements and challenges are also discussed.
Highlights
The development of novel technological platforms in molecular biology has produced a large amount of data about different aspects of the omic world [1]
The need for the development of novel approaches and methods to manage, store, and analyze this data arose [2,3,4]. This has caused the rise of a novel discipline, often referred to as computational systems biology or network systems biology, in which computer science, bioinformatics, and mathematical modeling play a synergistic role in the interpretation of large datasets belonging to different data sources [5, 6]
Examples of such platforms are microarray for studying the expression of messenger RNA [9, 10] and microRNA [11], genomic microarrays for studying copy number variations (CNV) or single nucleotide polymorphisms (SNP), novel microarrays for studying non-coding RNAs, genomic arrays for pharmacogenomics studies [12, 13], and novel next-generation sequencing (NGS) techniques
Summary
The development of novel technological platforms in molecular biology has produced a large amount of data about different aspects of the omic world [1]. A general model for integrating miRNA, mRNA, and TF data All the approaches here discussed present some main characteristics They have an internal knowledge base of associations extracted from literature and databases. Main differences among the approaches are represented by the association databases that are used This internal knowledge base is used for guiding the analysis of experimental data. It receives as input mRNA and miRNA expression data, obtained from samples that are subdivided into two classes (e.g., controls vs cases). Associations among mRNA and miRNA are derived considering their individual expression levels (i.e., considering pairs of mRNA-miRNA whose regulation is inversely correlated) or through their target interactions—via functional analysis through literature and databases. Henriksen et al [57] applied an integrated approach of analysis to identify miRNA-mRNA regulatory networks that are involved in glioma, a primary
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