Application of unstructured kinetic models to predict microcystin biodegradation: Towards a practical approach for drinking water treatment

Abstract

Biological drinking water treatment technologies offer a cost-effective and sustainable approach to mitigate microcystin (MC) toxins from harmful algal blooms. To effectively engineer these systems, an improved predictive understanding of the bacteria degrading these toxins is required. This study reports an initial comparison of several unstructured kinetic models to describe MC microbial metabolism by isolated degrading populations. Experimental data was acquired from the literature describing both MC removal and cell growth kinetics when MC was utilized as the primary carbon and energy source. A novel model-data calibration approach melding global single-objective, multi-objective, and Bayesian optimization in addition to a fully Bayesian approach to model selection and hypothesis testing were applied to identify and compare parameter and predictive uncertainties associated with each model structure. The results indicated that models incorporating mechanisms of enzyme-MC saturation, affinity, and cooperative binding interactions of a theoretical single, rate limiting reaction accurately and reliably predicted MC degradation and bacterial growth kinetics. Diverse growth characteristics were observed among MC degraders, including moderate to high maximum specific growth rates, very low to substantial affinities for MC, high yield of new biomass, and varying degrees of cooperative enzyme-MC binding. Model predictions suggest that low specific growth rates and MC removal rates of degraders are expected in practice, as MC concentrations in the environment are well below saturating levels for optimal growth. Overall, this study represents an initial step towards the development of a practical and comprehensive kinetic model to describe MC biodegradation in the environment.