A bayesian approach for parameterizing and predicting plasmid conjugation dynamics.

ABSTRACT: Population dynamic models that explain and predict the spread of conjugative plasmids are pivotal for understanding microbial evolution and engineering microbiomes. However, prediction uncertainty of these models has rarely been assessed. We adopt a Bayesian approach, employing Markov Chain Monte Carlo (MCMC), to parameterize and model plasmid conjugation dynamics. This approach treats model parameters as random variables whose probability distributions are informed by data on plasmid population dynamics. These distributions allow us to estimate credible intervals of the model's parameters and predictions. We validated this approach using synthetic population dynamic data with known parameter values and experimental population dynamic data of mini-RK2, a miniaturized counterpart of the wel

SUBMITTER: Kumsuwan S 

PROVIDER: S-EPMC11876642 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature