Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA.
Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA.
Water Res. 2019 Feb 1;149:617-631. doi: 10.1016/j.watres.2018.11.014. Epub 2018 Nov 12.
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.
生物饮用水处理技术为减轻有害藻类水华产生的微囊藻毒素 (MC) 提供了一种具有成本效益和可持续性的方法。为了有效地设计这些系统，需要对降解这些毒素的细菌有更好的预测性理解。本研究初步比较了几种非结构化动力学模型，以描述分离降解种群对 MC 的微生物代谢。实验数据来自文献，描述了将 MC 用作主要碳源和能源时的 MC 去除和细胞生长动力学。应用了一种新颖的模型-数据校准方法，融合了全局单目标、多目标和贝叶斯优化，以及完全贝叶斯方法进行模型选择和假设检验，以确定和比较与每个模型结构相关的参数和预测不确定性。结果表明，包含酶-MC 饱和、亲和力和协同结合相互作用机制的模型能够准确可靠地预测 MC 降解和细菌生长动力学。MC 降解菌表现出不同的生长特征，包括中等至高的最大比生长速率、对 MC 的极低至相当高的亲和力、新生物质的高产量以及协同酶-MC 结合的不同程度。模型预测表明，在实际中预计降解菌的比生长速率和 MC 去除速率较低，因为环境中的 MC 浓度远低于最佳生长的饱和水平。总的来说，这项研究代表了朝着开发实用和全面的动力学模型描述环境中 MC 生物降解迈出的初步一步。
