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Bayesian simulation of ammonium oxidation in microcosm experiments: Modeling code and data

Authors: Störiko, Anna; Wang, Zhe; Jakobs, Aileen; Straub, Daniel; Mellage, Adrian; Cirpka, Olaf A.; Pagel, Holger; +1 Authors

Bayesian simulation of ammonium oxidation in microcosm experiments: Modeling code and data

Abstract

This repository contains source code and data to simulate microcosm experiments with a nitrifying community from streambed sediments. The publication accompanies the manuscript Linking abundance and activity of ammonia-oxidizing bacteria and archaea in an agriculturally impacted first-order stream. The reaction model describes the conversion of ammonium to nitrate, and the growth of ammonia-oxidizing archaea and bacteria in incubations of streambed sediments. It simulates different experimental conditions with or without the amendment of ammonium as substrate and two nitrification inhibitors (acetylene and 1-octyne). Model parameters are inferred with a Bayesian modeling framework that accounts for uncertainty of parameters and data, using experimental data from microcosm incubations. The source code is written in Python, using the library PyMC for parameter estimation. Experimental data (ammonium and nitrate concentrations, qPCR data of amoA and 16S rRNA genes) are included in the upload, as well as the simulation results and posterior parameter samples.

All source code and documentation files (files ending with .py and .md) are under an MIT license (see LICENSE file). Experimental data and output data are licensed under the Creative Commons Attribution 4.0 International License.

Keywords

PyMC, qPCR, reaction model, ammonia-oxidizing archaea, Bayesian inference, ammonia-oxidizing bacteria, nitrification

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average