COMPUTATIONAL ELUCIDATION OF SIGNAL TRANSDUCTION NETWORKS: FROM DOCKING TO BIOCHEMICAL PATHWAY MAPPING IN METABOLIC DISORDERS

Abstract

Cellular signal transduction networks provide the molecular infrastructure that enables cells to sense and respond to internal and external cues, thereby coordinating biochemical pathways and preserving physiological homeostasis. Perturbations in these signaling architectures are a defining hallmark of metabolic diseases such as diabetes, obesity, and dyslipidemia. With the growth of computational biology, these complex signaling cascades can now be interrogated through integrative in-silico strategies that combine molecular docking, network pharmacology, and biochemical pathway analysis. Molecular docking is employed to predict protein–ligand recognition, revealing candidate targets and binding strengths associated with dysfunctional signaling nodes. Network-centric modeling, informed by transcriptomic and proteomic datasets, reconstructs hierarchical signaling layers, identifies key regulatory hubs, and captures cross-talk between metabolic circuits and stress-response pathways. In parallel, computational pathway enrichment and dynamic simulation approaches illuminate the global consequences of molecular perturbations and therapeutic modulation. The fusion of multi-omics information with structure-based drug design supports the ranking of bioactive molecules capable of re-tuning pivotal components of insulin, AMPK, and mTOR pathways. Collectively, this in-silico framework bridges atomic-scale interaction profiling with systems-level biology, enabling predictive modeling of disease trajectories and therapeutic responses. Consequently, computational interrogation of signal transduction networks represents a powerful paradigm in metabolic disease research, accelerating biomarker discovery, mechanism-guided drug development, and precision medicine initiatives.