Integrative network analysis reveals organizational principles of the endocannabinoid system.

backgroundThe endocannabinoid system (ECS) is a complex signaling network that regulates diverse physiological processes, including pain, mood, metabolism, and immune response, through coordinated interactions among receptors, enzymes, and lipid-derived ligands. Although individual ECS components have been extensively studied, the integrated systems-level organization and structural dependencies of the ECS remain insufficiently characterized in a unified network context. Here, we present a computational, network-based systems analysis of the ECS that integrates protein-protein and protein-chemical interactions into a unified interaction framework, enabling the identification of components that occupy structurally prominent positions in the network, with potential relevance to the role of ECS in diverse physiological processes and therapeutic contexts.

methodsWe constructed integrated ECS networks by combining experimentally validated protein-protein and protein-chemical interactions from multiple public databases. Network analyses were performed using centrality metrics, community detection algorithms, and targeted perturbations of highly ranked nodes to assess structural organization, modular architecture, and redistribution of topological influence.

resultsCentrality analyses systematically identified nodes with high topological prominence across the ECS network. Canonical receptors cannabinoid receptor 1 (CB

conclusionBy mapping the ECS as an integrated interaction network, this study provides a structural framework for understanding how receptors, enzymes, and ligands collectively shape ECS organization. Our results demonstrate that network analysis can identify structurally influential components within the ECS, highlighting nodes whose importance emerges from the overall network organization. The identification of highly ranked and perturbation-sensitive nodes offers a systematic basis for prioritizing underexplored components for hypothesis-driven experimental investigation and pharmacological study. More broadly, this work establishes a network-based foundation for expanding ECS modeling to incorporate additional molecular entities, interaction directionality, signaling dynamics, and tissue- or context-specific interactions, thereby informing future therapeutic strategies targeting the ECS and its interacting molecular pathways across diverse physiological processes and disease pathways.