Artificial Intelligence-Assisted Systems Biology Reconstruction of Diabetic Bone Marrow Dysfunction

Abstract

This study aimed to reconstruct a regulatory network suitable for systems biology analysis of diabetic bone marrow. Three artificial intelligence large language models independently analysed 18 Discovery and 43 Validation publications. Investigator guided integration produced a frozen network of 62 biological entities and 79 directed interactions. Open source software Gephi created a visual representation before and after main node removal. The plausibility of the reconstructed interactions was tested against independent curated knowledge resources, including the Kyoto Encyclopedia of Genes and Genomes, connectomeDB2025, Reactome, and Gene Ontology. Further analysis examined physiological correspondence, missing edge sensitivity, and graph constrained interrogation. Functional modules encompassed endothelial perivascular support, stem cell retention and trafficking, neural circadian regulation, inflammatory myelopoiesis, metabolic stress, and epigenetic regulation. They intersected through shared effects on endothelial function and stem cell behaviour. The endothelium was the principal connecting node. Computational removal of the endothelial node increased network fragmentation and diameter, while average directed path length remained essentially unchanged. External resources supported 60/79 interactions (75.9%); 19 remained unresolved, with none classified as contradictory. Complementary assessments supported biological plausibility and faithful interrogation of the reconstructed network. This investigator led dialogue with artificial intelligence provides a foundation for a systems level understanding of diabetic bone marrow dysfunction. The approach may facilitate the generation of testable hypotheses and the identification of mechanistic targets for coordinated restoration of disrupted niche interactions.