Bayesian modeling of bacterial growth for multiple populations

TL;DR

This paper introduces Bayesian methods for modeling bacterial growth across multiple populations under varying environmental conditions, utilizing neural networks and Gompertz-based models to improve prediction accuracy and interpretability.

Contribution

It develops two novel models combining neural networks with Bayesian inference to better predict bacterial growth under diverse conditions, addressing tuning challenges.

Findings

Bayesian neural network model effectively predicts bacterial growth.

The semi-parametric Gompertz model captures environmental influences.

Models demonstrated on Listeria monocytogenes data show improved accuracy.

Abstract

Bacterial growth models are commonly used for the prediction of microbial safety and the shelf life of perishable foods. Growth is affected by several environmental factors such as temperature, acidity level and salt concentration. In this study, we develop two models to describe bacterial growth for multiple populations under both equal and different environmental conditions. First, a semi-parametric model based on the Gompertz equation is proposed. Assuming that the parameters of the Gompertz equation may vary in relation to the running conditions under which the experiment is performed, we use feedforward neural networks to model the influence of these environmental factors on the growth parameters. Second, we propose a more general model which does not assume any underlying parametric form for the growth function. Thus, we consider a neural network as a primary growth model which includes the influencing environmental factors as inputs to the network. One of the main disadvantages of neural networks models is that they are often very difficult to tune, which complicates fitting procedures. Here, we show that a simple Bayesian approach to fitting these models can be implemented via the software package WinBugs. Our approach is illustrated using real experimental Listeria monocytogenes growth data.