Dataset Information

Enabling population protein dynamics through Bayesian modeling.

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 and implementation

R code and preprocessed data are available from zenodo.org. Raw data are available from panoramaweb.org.

SUBMITTER: Lehmann S

PROVIDER: S-EPMC11335370 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

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 J

Bioinformatics (Oxford, England) 20240801 8

<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 tha ...[more]

PMID: 39078204

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Project description:Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman's coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary period were related to climate change. [Coalescent; effective population size; Gaussian Markov random fields; phylodynamics; phylogenetics; population genetics.

Dataset Information

Enabling population protein dynamics through Bayesian modeling.

Motivation

Results

Availability and implementation

Publications

Enabling population protein dynamics through Bayesian modeling.

Similar Datasets

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