Systems biology and big data analytics

Abstract With the advancement of computational and sequencing methods, a large amount of biological data are generated by using various techniques, such as high-throughput screening, next-generation sequencing, Chipseq analysis, microarray analysis, and mass spectroscopy. These generated data sets (genome, transcriptome, proteome, and metabolome) are very complex and provide a holistic view of the biological mechanisms. The biological data have genes, proteins, RNA, and metabolome, which are interconnected with each other and regulate each other’s activation, repression, and in general the expression. Hence, dealing with complex biological networks and extracting the meaningful picture requires newly emerging techniques, such as systems biology. It is an emerging field that provides a top-down approach (whole cell to gene level) for big data analysis. However, the bottom-up approach is also applicable and implemented in systems biology. Hence, in this chapter, we have summarized various big data resources and how that data can be used for the systems biology analysis.