Metabolome Analysis: Disease Biomarker Discovery

Abstract

High-throughput technologies have revolutionized the genomic era, which requires an efficient way to perform analyses and thus correctly interpret them. Thus, network biology provides the effective way to analyze the high-throughput data as it involves the complete system and therefore decreases the chances of missing any kind of interrelated information. Network biology has been widely used in almost every field whether it include genes, transcriptomes, proteins, or the metabolomes. Network biology has been widely used for the interaction studies and analysis in modern era. Studies associated with biological networks, their modeling, analysis, and visualization is imperative to the biological world. The advancements in network biology helped to overcome and understand the bimolecular complexities that occur inside the cell however poses a serious challenge if to be captured in vivo. Therefore, efforts have been induced in a way to get maximum possible interactions that are significant in maintaining the potential mechanisms with the aid of network biology. However studies have been widely conducted to study the biochemical networks (that include genes, transcripts, proteins, and metabolome) but major emphasis has been given to the metabolic networks as they not only cover the metabolic components but also the gene regulatory mechanism as well as the protein component. To find the efficient diagnostic mechanisms for the complex diseases (diabetes, cardiovascular diseases (CVD), and cancers) metabolic biomarkers can prove to be the most promising biomarkers. In this article we are covering the basics of the biological networks with special emphasis on the metabolic one, computational methods available to handle the metabolomic data, metabolome as a potential biomarkers, biomarker discovery for complex diseases through gene expression and metabolic network studies, and a case study using computational pipelines. This article therefore deals with the metabolic networks, the advancements introduced so far in this field, its potential applications in biomarkers discovery through computational pipelines, and its role in various disease studies.