METABOLIC PATHWAY ANALYSIS: TOOLS AND APPLICATIONS FOR DISEASE DIAGNOSIS AND EMERGENCY MANAGEMENT IN LABORATORY MEDICINE

Saadi Saad Alanazi*, Sultan Safah A. Alharbi, Dr. Tahani Mohammed Alqurashi, Mutlaq Gatar N. Alruwas, Abdullah Ibrahim Al Eissa, Khalid Saad Matar Alshammari, Fahed Mohammed Alshammri, Abdulaziz Abdullah Mughiran Alharbi, Raud Mater Husayban Al-Mutairi

ABSTRACT

Background: Metabolic pathways, comprising intricate biochemical networks, are crucial for maintaining homeostasis, energy production, and cellular functionality. Understanding these pathways is vital for elucidating physiological processes and disease mechanisms, including cancer, infectious diseases, and metabolic disorders. However, their complexity—characterized by dynamic interactions and regulatory systems—poses significant analytical challenges. Advances in computational modeling, high-throughput techniques, and multi-omics data integration have revolutionized the study of cellular metabolism. Aim: This study aims to provide a comprehensive overview of metabolic pathway analysis techniques and tools, highlighting their applications in synthetic biology, drug discovery, and biomedical research. Methods: The study combines experimental approaches such as isotope labeling, mass spectrometry, and nuclear magnetic resonance (NMR) with computational strategies like flux balance analysis (FBA), machine learning algorithms, and pathway enrichment analysis. It emphasizes the integration of omics data, including transcriptomics, proteomics, and metabolomics, for reconstructing and analyzing complex metabolic networks. Results: Key findings include insights into hereditary metabolic disorders, altered metabolic pathways in cancer (e.g., the Warburg effect), and pathway optimization for synthetic biology applications. Computational models, complemented by experimental validation, demonstrate the potential of metabolic pathway analysis in identifying novel biomarkers and therapeutic targets. Conclusion: Metabolic pathway analysis is a powerful approach for understanding cellular mechanisms and disease etiologies. Despite significant advancements, challenges such as biological complexity, data integration, and model validation remain. Future directions include leveraging artificial intelligence and advanced technologies for more robust and precise analyses.

Keywords: disease biomarkers, metabolic pathway, systems biology, computational modeling, isotope labeling, omics integration, pathway analysis.