Graphical models for inference: A model comparison approach for analyzing bacterial conjugation

TL;DR

This paper introduces a model comparison approach using Bayesian networks to analyze bacterial conjugation, helping to identify the most accurate interaction mechanisms from observational data.

Contribution

It presents a novel method for analyzing bacterial conjugation by comparing structurally similar Bayesian network models with different interaction mechanisms.

Findings

Identified the most accurate models for bacterial conjugation mechanisms.

Demonstrated the approach on three experimental trials.

Provided insights into factors affecting bacterial conjugation.

Abstract

We present a proof-of-concept of a model comparison approach for analyzing spatio-temporal observations of interacting populations. Our model variants are a collection of structurally similar Bayesian networks. Their distinct Noisy-Or conditional probability distributions describe interactions within the population, with each distribution corresponding to a specific mechanism of interaction. To determine which distributions most accurately represent the underlying mechanisms, we examine the accuracy of each Bayesian network with respect to observational data. We implement such a system for observations of bacterial populations engaged in conjugation, a type of horizontal gene transfer that allows microbes to share genetic material with nearby cells through physical contact. Evaluating cell-specific factors that affect conjugation is generally difficult because of the stochastic nature of the process. Our approach provides a new method for gaining insight into this process. We compare eight model variations for each of three experimental trials and rank them using two different metrics