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The mysterious connection between genes and metabolism: Why are metabolic pathways so critical?
In molecular biology, the term "pathway" is used to describe an elaborately organized schematic diagram of a portion of a molecular physiological machinery, including metabolic pathways and signal transduction pathways. Metabolic pathways describe enzymatic reaction processes within cells or tissues, while signal transduction pathways simulate a regulatory process. Researchers are continually exploring the complexity of these pathways and trying to understand the roles they play in biology.
A typical pathway model begins with an extracellular signaling molecule that activates a specific receptor, triggering a series of molecular interactions.
Pathways are typically represented as graphical representations of gene, protein, and/or small molecule nodes, connected by known functional associations. In many cases, these pathways exhibit complex topologies, including loops and alternative pathways. This type of analysis helps explore changes in gene expression and thus shed light on its biological activity.
However, pathway analysis is most often used for the initial characterization and interpretation of known experimental or pathological conditions, which are often studied using omics tools or genome-wide association studies.
Researchers can perform pathway analysis using high-throughput biological data, including high-throughput sequencing data and microarray data. Before pathway analysis can be performed, each gene must be assessed for changes, which may involve quantitative (differential expression analysis) or qualitative analysis (detection of somatic variants or mapping of neighboring genes to disease-associated single nucleotide polymorphisms). This information can help researchers understand which functional gene sets (FGS) are strongly altered in the experiment and further reveal their potential biomarkers in specific diseases.
Pathway content, structure, format, and functionality vary among different database resources, such as KEGG, WikiPathways, and Reactome.
When performing pathway analysis, common methods include over-representation analysis (ORA), functional category scoring (FCS), pathway topology analysis (PTA) and network enrichment analysis (NEA). These methods come from different analytical backgrounds and are able to identify key genes in gene sets and their associated pathways based on high-throughput data.
Early studies have shown that pathway analysis can reveal genes important in rapid pathological processes with both flexibility and precision.
In terms of commercial solutions, although there are many open source tools and public repositories, various companies also offer proprietary pathway analysis software and databases. These tools not only improve research efficiency, but also systematize biological knowledge and provide powerful analytical capabilities.
Although pathway analysis has broad application potential, its limitations cannot be ignored. Due to reliance on annotations in existing databases, interpretation of results from pathway analyses must be done with caution, as much information may lack details on cell type or developmental context.These commercial products typically promote their own proprietary channels and networks, and the choice of such products may be influenced by the user's skills, financial and time resources.
So, what challenges will researchers face in future analyses to better understand the mysterious connection between genes and metabolism?
