Mutation and selection in bacteria: modelling and calibration

TL;DR

This paper develops models for bacterial evolution considering mutation and selection, introducing algorithms for model validation and parameter estimation, and applies them to real Campylobacter jejuni data.

Contribution

It presents an efficient algorithm for testing mutation models and an ABC method for estimating fitness parameters in selection-mutation models.

Findings

Model explains experimental bacterial data effectively.

Algorithm successfully estimates fitness parameters.

Provides insights into bacterial evolution dynamics.

Abstract

Temporal evolution of a clonal bacterial population is modelled taking into account reversible mutation and selection mechanisms. For the mutation model, an efficient algorithm is proposed to verify whether experimental data can be explained by this model. The selection-mutation model has unobservable fitness parameters and, to estimate them, we use an Approximate Bayesian Computation (ABC) algorithm. The algorithms are illustrated using in vitro data for phase variable genes of Campylobacter jejuni.