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 here a new modeling approach to capture the highly relevant notion of population protein dynamics. Using two data sets, we show that models inspired by population pharmacokinetics can accurately capture protein turnover within a cohort of individuals, even in presence of substantial inter-individual variability. R scripts --------- The script BUGS-SILK-7.R computes protein dynamics models for one patient at a time. It was used to explore the possibility to apply Bayesian modeling to SILK data. The script BUGS-pop-models.R computes the population protein dynamics Bayesian models. Note that the script should work provided they are run with all the data folders being subfolders of the working directory. Data folders ------------ HRMS (unbiased proteomics protocol, see Lehmann, et al., Anal Chem, 2019) folders contain the results of the QNB analysis. One folder per fluid (CSF-HRMS-05-2021 & plasma-HRMS-05-2021). Analysis results are provided in a tabulated text file in each case with individual protein parameters. These parameter values are used to build prior distributions in the new paper. TQ (targeted MRM protocol) folders contain the results of the QNB analysis, which are the input data for this paper. These are stored in the CSF-TQ-05-2021/models & plasma-TQ-05-2021/models folders. In each case, a plot of the protein model is provided for reference (.pdf files) as well as a table listing each observed RIA (-final-selection.txt files) and another table providing the QNB model parameters (-bootstrap.txt files). In addition, thq TQ folders contain the results of the Bayesian population modeling. The folders CSF-TQ-05-2021/BUGS-models & plasma-TQ-05-2021/BUGS-models contain the models obtained with the non-informative prior.The folders CSF-TQ-05-2021/BUGS-models-prior & plasma-TQ-05-2021/BUGS-models-prior contain the models obtained with the informative prior built from the HRMS data. HRMS and TQ folders are respectively zipped under HRMS.folders.plasma-and-csf.zip and TQ.folders.plasma-and-csf.zip files.