Université de Montpellier, Montpellier, 34000, France.
LBPC-PPC CHU Montpellier, INM INSERM, Montpellier, 34000, France.
Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae484.
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.
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.
R code and preprocessed data are available from zenodo.org. Raw data are available from panoramaweb.org.
了解患者体内蛋白质的动态变化或周转率，可以提供与某些疾病、药物疗效或生物过程相关的宝贵信息。已经开发出大量的实验和计算方法，包括我们在对体内随访的人类患者的情况下所开发的方法。更进一步，我们提出了一种新的建模方法，使用贝叶斯方法来捕捉群体蛋白质动力学。
使用两个数据集，我们证明了受群体药代动力学启发的模型可以准确地捕捉队列内的蛋白质周转率，并解释个体间的差异。这些模型为寻找疾病中动态变化或生物标志物的比较研究铺平了道路。
R 代码和预处理数据可从 zenodo.org 获得。原始数据可从 panoramaweb.org 获得。
