Enabling Population Protein Dynamics Through Bayesian Modeling

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Lehmann, Sylvain | Vialaret, Jérôme | Gabelle, Audrey | Bauchet, Luc | Villemin, Jean-Philippe | Hirtz, Christophe | Colinge, Jacques

Edité par CCSD ; Oxford University Press (OUP) -

International audience. Abstract Motivation 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. Results 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. Availability R code and preprocessed data are available from zenodo.org. Raw data are available from panoramaweb.org. Supplementary information Supplementary data are available at Bioinformatics online.

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