Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ

Published: March 4, 2024

Abstract: Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While Genome-scale Metabolic Models (GEMs) show promise systems medicine by integrating various data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce comprehensive pipeline GEMs with plasma metabolome. This constructs case-specific using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling an in-house developed algorithm for rational exploration of sampled space solutions, enhance integration accuracy while improving performance. Model characterization involves task analysis combination clustering methods identify critical cellular functions. The new was applied cohort trauma patients investigates shock-induced endotheliopathy metabolome By analyzing endothelial cell metabolism comprehensively, identified substrates contributed development targeted therapeutic strategies. Our study demonstrates efficacy into models analyze disease contexts. approach offers deeper understanding metabolic dysregulations provides insights diseases components potential treatments.

Language: Английский

Special Issue “Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine”

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(19), P. 10579 - 10579

Published: Oct. 1, 2024

The field of bioinformatics has made remarkable strides in recent years, revolutionizing our approach to understanding and treating human diseases [...].

Language: Английский