Modeling physiological processes that relate toxicant exposure and bacterial population dynamics

• Published in: PloS one 2012-02, Vol.7 (2), p.e26955
• Main Authors: Klanjscek, Tin, Nisbet, Roger M, Priester, John H, Holden, Patricia A
• Format: Article
• Language: English
• Subjects: Acclimation; Acclimatization; Aging; Bacteria; Bacteria - drug effects; Bacteria - growth & development; Bacteriology; Biodegradation; Biology; Cadmium; Cadmium - toxicity; Cell division; E coli; Earth Sciences; Ecotoxicology; Ecotoxicology - methods; Energy budget; Environment models; Environmental degradation; Environmental science; Environmental toxicology; Escherichia coli; Exposure; Food; Growth patterns; Health aspects; Lag time; Listeria; Marine biology; Mathematics; Metabolism; Metabolites; Microbiology; Microorganisms; Models, Theoretical; Mortality; Optical density; Oxygen; Parameter estimation; Physiological aspects; Physiological effects; Physiology; Population biology; Population Dynamics; Population growth; Predictions; Proteins; Pseudomonas aeruginosa; Pseudomonas aeruginosa - drug effects; Pseudomonas aeruginosa - growth & development; Pseudomonas putida; Reactive oxygen species; Staphylococcus aureus; Toxicity; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0026955

Quantifying effects of toxicant exposure on metabolic processes is crucial to predicting microbial growth patterns in different environments. Mechanistic models, such as those based on Dynamic Energy Budget (DEB) theory, can link physiological processes to microbial growth.Here we expand the DEB framework to include explicit consideration of the role of reactive oxygen species (ROS). Extensions considered are: (i) additional terms in the equation for the "hazard rate" that quantifies mortality risk; (ii) a variable representing environmental degradation; (iii) a mechanistic description of toxic effects linked to increase in ROS production and aging acceleration, and to non-competitive inhibition of transport channels; (iv) a new representation of the "lag time" based on energy required for acclimation. We estimate model parameters using calibrated Pseudomonas aeruginosa optical density growth data for seven levels of cadmium exposure. The model reproduces growth patterns for all treatments with a single common parameter set, and bacterial growth for treatments of up to 150 mg(Cd)/L can be predicted reasonably well using parameters estimated from cadmium treatments of 20 mg(Cd)/L and lower. Our approach is an important step towards connecting levels of biological organization in ecotoxicology. The presented model reveals possible connections between processes that are not obvious from purely empirical considerations, enables validation and hypothesis testing by creating testable predictions, and identifies research required to further develop the theory.