Bayesian inference for kinetic models of biotransformation using a generalized rate equation

Shanshan Ying; Jiangjiang Zhang; Lingzao Zeng; Jiachun Shi; Laosheng Wu

doi:10.1016/j.scitotenv.2017.03.003

Bayesian inference for kinetic models of biotransformation using a generalized rate equation

Sci Total Environ. 2017 Jul 15:590-591:287-296. doi: 10.1016/j.scitotenv.2017.03.003. Epub 2017 Mar 6.

Authors

Shanshan Ying¹, Jiangjiang Zhang¹, Lingzao Zeng², Jiachun Shi¹, Laosheng Wu³

Affiliations

¹ College of Environmental and Natural Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, 310058 Hangzhou, China.
² College of Environmental and Natural Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, 310058 Hangzhou, China. Electronic address: lingzao@zju.edu.cn.
³ College of Environmental and Natural Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, 310058 Hangzhou, China; Department of Environmental Science, University of California, Riverside, CA 92521, United States.

PMID: 28279533
DOI: 10.1016/j.scitotenv.2017.03.003

Abstract

Selecting proper rate equations for the kinetic models is essential to quantify biotransformation processes in the environment. Bayesian model selection method can be used to evaluate the candidate models. However, comparisons of all plausible models can result in high computational cost, while limiting the number of candidate models may lead to biased results. In this work, we developed an integrated Bayesian method to simultaneously perform model selection and parameter estimation by using a generalized rate equation. In the approach, the model hypotheses were represented by discrete parameters and the rate constants were represented by continuous parameters. Then Bayesian inference of the kinetic models was solved by implementing Markov Chain Monte Carlo simulation for parameter estimation with the mixed (i.e., discrete and continuous) priors. The validity of this approach was illustrated through a synthetic case and a nitrogen transformation experimental study. It showed that our method can successfully identify the plausible models and parameters, as well as uncertainties therein. Thus this method can provide a powerful tool to reveal more insightful information for the complex biotransformation processes.

Keywords: Bayesian model selection; N transformation; Parameter estimation; Rate equation.