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The Lambda Select cII Mutation Detection System

The Lambda Select cII Mutation Detection System

Published on: April 26, 2018

Mutation and Selection in Bacteria: Modelling and Calibration.

C D Bayliss¹, C Fallaize², R Howitt²

¹Department of Genetics, University of Leicester, Leicester, LE1 7RH, UK.

Bulletin of Mathematical Biology

|November 16, 2018

Summary

This summary is machine-generated.

This study models bacterial population dynamics using reversible mutation and selection. An efficient algorithm verifies mutation models, while Approximate Bayesian Computation estimates fitness parameters for selection-mutation models, using Campylobacter jejuni data.

Keywords:

Approximate Bayesian computation Phase variable genes Population genetics Stochastic modelling

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Published on: September 27, 2016

Area of Science:

Microbiology
Evolutionary Biology
Computational Biology

Background:

Bacterial populations evolve through mutation and selection.
Understanding these evolutionary mechanisms is crucial for microbiology and medicine.
Phase variable genes in Campylobacter jejuni are known to exhibit rapid evolution.

Purpose of the Study:

To develop and validate computational models for bacterial evolution.
To propose an efficient algorithm for testing reversible mutation models against experimental data.
To estimate unobservable fitness parameters in selection-mutation models.

Main Methods:

Modeling the temporal evolution of clonal bacterial populations.
Developing an efficient algorithm to verify mutation models.
Utilizing Approximate Bayesian Computation (ABC) for parameter estimation in selection-mutation models.
Applying the algorithms to in vitro data of Campylobacter jejuni phase variable genes.

Main Results:

An efficient algorithm was developed to assess the validity of mutation models.
Approximate Bayesian Computation successfully estimated fitness parameters for the selection-mutation model.
The proposed algorithms were effectively demonstrated using experimental data.

Conclusions:

The developed models and algorithms provide a robust framework for studying bacterial evolution.
The methods allow for the quantitative analysis of mutation and selection in microbial populations.
This approach is applicable to understanding the evolution of specific genes, such as phase variable genes in Campylobacter jejuni.