INDISIM-Paracoccus, an individual-based and thermodynamic model for a denitrifying bacterium

Araujo-Granda, Pablo, Ginovart, Marta, Gras, Anna and Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435 (2016) INDISIM-Paracoccus, an individual-based and thermodynamic model for a denitrifying bacterium. Journal of Theoretical Biology, 403. 45–58. ISSN 0022-5193

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Abstract

We have developed an individual-based model for denitrifying bacteria. The model, called INDISIM-Paraccocus, embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM, and is designed to simulate the bacterial cell population behaviour and the product dynamics within the culture. The INDISIM-Paracoccus model assumes a culture medium containing succinate as a carbon source, ammonium as a nitrogen source and various electron acceptors such as oxygen, nitrate, nitrite, nitric oxide and nitrous oxide to simulate in continuous or batch culture the different nutrient-dependent cell growth kinetics of the bacterium Paracoccus denitrificans. The individuals in the model represent microbes and the individual-based model INDISIM gives the behaviour-rules that they use for their nutrient uptake and reproduction cycle. Three previously described metabolic pathways for P. denitrificans were selected and translated into balanced chemical equations using a thermodynamic model. These stoichiometric reactions are an intracellular model for the individual behaviour-rules for metabolic maintenance and biomass synthesis and result in the release of different nitrogen oxides to the medium. The model was implemented using the NetLogo platform and it provides an interactive tool to investigate the different steps of denitrification carried out by a denitrifying bacterium. The simulator can be obtained from the authors on request.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology > Computational biology of RNA (former - to 2018) Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	Pure Connector
Date Deposited:	13 May 2016 16:00
Last Modified:	14 Jun 2023 12:33
URI:	https://ueaeprints.uea.ac.uk/id/eprint/58741
DOI:	10.1016/j.jtbi.2016.05.017

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