# Enabling population protein dynamics through Bayesian modeling

## TL;DR

This paper introduces a Bayesian modeling approach to study protein turnover in populations, enabling insights into disease dynamics and biomarker discovery.

## Contribution

A novel Bayesian modeling approach is introduced for capturing population-level protein dynamics and inter-individual variability.

## Key findings

- Bayesian models inspired by population pharmacokinetics accurately capture protein turnover in cohorts.
- The approach accounts for inter-individual variability, enabling comparative studies of altered dynamics in diseases.

## Abstract

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.