A bayesian approach for parameterizing and predicting plasmid conjugation dynamics.

Area of Science:

• Microbiology
• Computational Biology
• Evolutionary Biology

Background:

• Conjugative plasmids are key drivers of microbial evolution and microbiome engineering.
• Accurate population dynamic models are crucial for understanding plasmid spread.
• Assessing prediction uncertainty in these models remains a challenge.

Purpose of the Study:

• To develop and validate a Bayesian approach for parameterizing and modeling plasmid conjugation dynamics.
• To quantify prediction uncertainty in population dynamic models of conjugative plasmids.
• To assess the impact of long-term data on model parameter estimation and prediction accuracy.

Main Methods:

• Bayesian inference employing Markov Chain Monte Carlo (MCMC) for model parameterization.
• Utilizing synthetic data with known parameters for validation.
• Applying the method to experimental population dynamic data of the mini-RK2 plasmid.

Main Results:

• The Bayesian MCMC approach accurately estimated parameters for synthetic data.
• Model predictions demonstrated robustness across various time scales and initial conditions.
• Incorporating long-term data improved parameter estimation precision and prediction accuracy for experimental data, though some parameter identifiability issues were noted.

Conclusions:

• The developed Bayesian method effectively quantifies uncertainty in plasmid conjugation dynamics.
• This approach enhances the reliability of predictions for microbial evolution and microbiome engineering.
• The methodology shows potential for broader application to other mobile genetic elements.