Version 1
: Received: 1 March 2024 / Approved: 4 March 2024 / Online: 4 March 2024 (14:02:11 CET)
How to cite:
Silva-Lance, F.; Montejano-Montelongo, I.; Bautista, E.; Nielsen, L.K.; Johansson, P.I.; Marin de Mas, I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Preprints2024, 2024030136. https://doi.org/10.20944/preprints202403.0136.v1
Silva-Lance, F.; Montejano-Montelongo, I.; Bautista, E.; Nielsen, L.K.; Johansson, P.I.; Marin de Mas, I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Preprints 2024, 2024030136. https://doi.org/10.20944/preprints202403.0136.v1
Silva-Lance, F.; Montejano-Montelongo, I.; Bautista, E.; Nielsen, L.K.; Johansson, P.I.; Marin de Mas, I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Preprints2024, 2024030136. https://doi.org/10.20944/preprints202403.0136.v1
APA Style
Silva-Lance, F., Montejano-Montelongo, I., Bautista, E., Nielsen, L.K., Johansson, P.I., & Marin de Mas, I. (2024). Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Preprints. https://doi.org/10.20944/preprints202403.0136.v1
Chicago/Turabian Style
Silva-Lance, F., Pär I. Johansson and Igor Marin de Mas. 2024 "Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ" Preprints. https://doi.org/10.20944/preprints202403.0136.v1
Abstract
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 in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigates shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical substrates and contributed to the development of targeted therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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