Advancements in Metabolic Pathway Analysis: Tools, Techniques, and Biomedical Applications- An Updated Review

Abstract

Background: Complex networks of biochemical events known as metabolic pathways are necessary for homeostasis, energy production, and cellular function. Understanding these pathways is essential to comprehending a variety of physiological functions and pathological states, including infectious illnesses, cancer, and metabolic disorders. Analysis of these networks is severely hampered by their complexity, which includes dynamic connections and regulatory systems. The area has been transformed by developments in computer modeling, high-throughput experimental methods, and multi-omics data integration, which have allowed for a greater understanding of cellular metabolism.

Aim: this study is to present a thorough examination of metabolic pathway analysis techniques and tools, with a focus on their uses in synthetic biology, drug development, and biomedical research.

Methods: In addition to experimental techniques including isotope labeling, mass spectrometry, and nuclear magnetic resonance (NMR), a mix of computational methods is covered, including flux balance analysis (FBA), machine learning algorithms, and pathway enrichment analysis. Reconstructing and studying intricate metabolic networks requires the integration of omics information, including transcriptomics, proteomics, and metabolomics.

Results: include insights into hereditary metabolic diseases, case studies showing changed metabolic pathways in cancer (such as the Warburg effect), and pathway optimization for bioproduction in synthetic biology. The promise of metabolic pathway analysis in locating new biomarkers and treatment targets is demonstrated by computational models and experimental validations.

Conclusion: metabolic pathway analysis is an effective method for figuring out how cells work and how diseases are caused. Even with the tremendous advancements, problems like biological complexity, data integration, and model validation still exist. Using artificial intelligence and cutting-edge technologies for more thorough and accurate assessments is one of the future directions.