Modeling physiological processes that relate toxicant exposure and bacterial population dynamics

Area of Science:

• Ecotoxicology
• Microbial Physiology
• Computational Biology

Background:

• Quantifying toxicant effects on microbial metabolism is vital for predicting growth.
• Dynamic Energy Budget (DEB) theory links physiological processes to microbial growth.

Purpose of the Study:

• Expand the DEB framework to include reactive oxygen species (ROS).
• Mechanistically describe toxicant effects on microbial physiology and growth.
• Improve predictions of microbial growth under toxicant exposure.

Main Methods:

• Extended DEB model with terms for hazard rate, environmental degradation, ROS production, aging, and acclimation lag time.
• Estimated model parameters using Pseudomonas aeruginosa growth data under seven cadmium exposure levels.
• Validated model predictions against observed growth patterns.

Main Results:

• The extended DEB model successfully reproduced Pseudomonas aeruginosa growth patterns across various cadmium concentrations.
• A single parameter set effectively modeled growth for all tested cadmium levels.
• Accurate predictions of bacterial growth were achieved for cadmium concentrations up to 150 mg/L.

Conclusions:

• The enhanced DEB model provides a mechanistic link between ROS, toxicant exposure, and microbial growth.
• This approach advances ecotoxicology by connecting biological organization levels and enabling hypothesis testing.
• The model identifies future research directions for refining toxicological predictions.