panoramaProteomicsJerome VIALARETHomo Sapienshttps://panoramaweb.org/Xpd6J5.urljerome_vialaret@yahoo.frMontpellier UniversityPanoramaPublic<h4>Motivation</h4>The knowledge of protein dynamics, or turnover, in patients provides invaluable information related to certain diseases, drug efficacy, or biological processes. A great corpus of experimental and computational methods has been developed, including by us, in the case of human patients followed in vivo. Moving one step further, we propose a novel modeling approach to capture population protein dynamics using Bayesian methods.<h4>Results</h4>Using two datasets, we demonstrate that models inspired by population pharmacokinetics can accurately capture protein turnover within a cohort and account for inter-individual variability. Such models pave the way for comparative studies searching for altered dynamics or biomarkers in diseases.<h4>Availability and implementation</h4>R code and preprocessed data are available from zenodo.org. Raw data are available from panoramaweb.org.Enabling population protein dynamics through Bayesian modeling.Lehmann Sylvain S, Vialaret Jérôme J, Gabelle Audrey A, Bauchet Luc L, Villemin Jean-Philippe JP, Hirtz Christophe C, Colinge Jacques JfalseStable Isotope Labeling by Amino acid in Vivo (SILAV)Intravenous administration of stable isotope labeled amino acid ((13)C6-leucine) to humans recently made it possible to study the metabolism of specific biomarkers in plasma using High Resolution mass spectrometry (MS). This labeling approach could be of great interest for monitoring many leucine-containing peptides in parallel, using HRMS. This will make it possible to quantify the rates of synthesis and clearance of a large range of proteins in humans with a view to obtaining new insights into protein metabolism processes and the pathophysiology of diseases such as Alzheimer's disease.Mon Nov 10 00:00:00 GMT 2025PXD046373960639078204