Computer modeling can be a useful tool in understanding the dynamics of bacterial population growth.
Yet, the variability and complexity of biological systems pose unique challenges in model building and adjustment.
Recent tools from Bayesian statistical inference can be brought together to solve these problems.
As an example, the authors modeled the development of biofilm in an industrial pilot drinking-water network.
The relationship between chlorine disinfectant, organic carbon, and bacteria concentrations was described by differential equations.
Using a Bayesian approach, they derived statistical distributions for the model parameters, on the basis of experimental data.
The model was found to adequately fit both prior biological information and the data, particularly at chlorine concentrations between 0.1 and 2 mg/liter.
Bacteria were found to have different characteristics in the different parts of the network.
The model was used to analyze the effects of various scenarios of water quality at the inlet of the network.
The biofilm appears to be very resistant to chlorine and confers a large inertia to the system.
Free bacteria are efficiently inactivated by chlorine, particularly at low concentrations of dissolved organic carbon.
Mots-clés Pascal : Eau potable, Réseau distribution, Bactérie, Modélisation, Modèle dynamique, Méthode Monte Carlo
Mots-clés Pascal anglais : Drinking water, Distribution network, Bacteria, Modeling, Dynamic model, Monte Carlo method
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0060763
Code Inist : 001D16A02. Création : 14/05/1998.