Biochemical Cascade - Pathway-Oriented Approaches

Pathway-Oriented Approaches

In the post-genomic age, high-throughput sequencing and gene/protein profiling techniques have transformed biological research by enabling comprehensive monitoring of a biological system, yielding a list of differentially expressed genes or proteins, which is useful in identifying genes that may have roles in a given phenomenon or phenotype. With DNA microarrays and genome-wide gene engineering, it is possible to screen global gene expression profiles to contribute a wealth of genomic data to the public domain. With RNA interference, it is possible to distill the inferences contained in the experimental literature and primary databases into knowledge bases that consist of annotated representations of biological pathways. In this case, individual genes and proteins are known to be involved in biological processes, components, or structures, as well as how and where gene products interact with each other. Pathway-oriented approaches for analyzing microarray data, by grouping long lists of individual genes, proteins, and/or other biological molecules according to the pathways they are involved in into smaller sets of related genes or proteins, which reduces the complexity, have proven useful for connecting genomic data to specific biological processes and systems. Identifying active pathways that differ between two conditions can have more explanatory power than a simple list of different genes or proteins. In addition, a large number of pathway analytic methods exploit pathway knowledge in public repositories such as Gene Ontology (GO) or Kyoto Encyclopedia of Genes and Genomes (KEGG), rather than inferring pathways from molecular measurements. Furthermore, different research focuses have given the word “pathway” different meanings. For example, ‘pathway’ can denote a metabolic pathway involving a sequence of enzyme-catalyzed reactions of small molecules, or a signaling pathway involving a set of protein phosphorylation reactions and gene regulation events. Therefore, the term “pathway analysis” has a very broad application. For instance, it can refer to the analysis physical interaction networks (e.g., protein–protein interactions), kinetic simulation of pathways, and steady-state pathway analysis (e.g., flux-balance analysis), as well as its usage in the inference of pathways from expression and sequence data. Several functional enrichment analysis tools and algorithms have been developed to enhance data interpretation. The existing knowledge base–driven pathway analysis methods in each generation have been summarized in recent literature.