The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).
TopIntroduction
Bioinformatics is the application of information technology in the field of molecular biology to manage process and analyze both genomic and molecular biological data. Its primary goal is to improve the understanding of biological processes (Nair, 2007). Bioinformatics is a very rich filed of research, in fact it includes so many research areas with diverse applications and according to Srikanth Aluru (2006), the major research areas in Bioinformatics are: Genome annotation (Kelley, MacCallum, & Sternberg, 2000), Sequence analysis (Durbin 1998, Sharif et al. 2015, Sharif et al. 2016, Tharwat et al. 2015), Computational evolutionary biology (Cosmides & Tooby, 1995), Gene expression analysis (Velculescu, Zhang, Vogelstein, & Kinzler, 1995), Analysis of regulation (Janssen, Jones, Jones, & Woods, 1988)and Protein expression analysis (Ghaemmaghami, et al., 2003). In addition, it includes analysis of mutations in cancer (Berrozpe, Schaeffer, Peinado, Real, & Perucho, 1994), Predictions of protein structure (Shortle, 2000), Comparative Genomics (Rubin, et al., 2000), Modeling biological systems (Haefner, 2005), High throughput image analysis (Dowsey, 2005), Protein-protein docking (Dominguez, Boelens, & Bonvin, 2003)and Microarrays (Churchill, 2002).
The microarray is one of the active research fields in bioinformatics. It’s a multiplex lab-on-a-chip that assays large amounts of biological material using high throughput screening methods (Culf, Cuperlovic-Culf, & Ouellette, 2006). Moreover, the microarrays experiment (Li & Li, 2008) starts with the sample preparation after that the hybridization, diction step and finally the data acquisition and analysis step. The microarray chips are scanned using special devices called Microarray Scanner. The microarray type depends on the biological material that assays. There are a number of types of microarrays including DNA microarrays (Gasch, et al., 2000), MMChips, for surveillance of microRNA populations (Meenakshisundaram, et al., 2009), Protein microarrays (Stoevesandt, Taussig, & He, 2009), Tissue microarrays (Kononen, et al., 1998), Cellular microarrays (Ma & Horiuchi, 2006), Chemical compound microarrays (Ma & Horiuchi, 2006), Antibody microarrays (Rivas, et al., 2008) and Carbohydrate arrays (glycoarrays) (Culf, Cuperlovic-Culf, & Ouellette, 2006).
The DNA Microarray technology is a high throughput experimental technique that can measure expression levels of hundreds of thousands of genes simultaneously. As an illustration, expression level of the gene is estimated by measuring the amount of mRNA for that gene. A gene is active if it is being transcribed; the more mRNA usually indicates more gene activity (Schena, Shalon, Davis, & Brown, 1995). The DNA microarray has become a useful technique in gene expression analysis for the development of new diagnostic tools and for the identification of disease genes and therapeutic targets for disease such as cancer. In fact, it’s the basis for a very efficient screening and diagnosis of disease in an early stage of development. Microarrays could also be expanded into a starting point for developing new treatments for various types of disease.