Mathematical modelling of bacterial mercury resistance
A mathematical model of mercury resistance was designed which describes the following reactions: the cellular uptake and volatilisation of Hg2+, binding of the DNA by the regulator, mer protein synthesis, and dilution of quantities by cell growth. A total of 66 biological experiments were then selec...
| Main Author: | |
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2015
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| Online Access: | https://eprints.nottingham.ac.uk/30521/ |
| _version_ | 1848794001602248704 |
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| author | Crossland, Richard J. |
| author_facet | Crossland, Richard J. |
| author_sort | Crossland, Richard J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A mathematical model of mercury resistance was designed which describes the following reactions: the cellular uptake and volatilisation of Hg2+, binding of the DNA by the regulator, mer protein synthesis, and dilution of quantities by cell growth. A total of 66 biological experiments were then selected from the scientific literature from studies of Tn21 and Tn501 in E. coli at 37 °C. These experiments were repeated in the computer simulation and the information from their 489 data points was incorporated into the 16 parameters of the model using the Metropolis-Hastings algorithm.
This model is very useful biology for four reasons. Firstly, it shows whether the data from existing biological experiments are consistent with each other or not. Secondly, it predicts the previously unknown concentrations of mer proteins in cells of each mercury phenotype. In addition, it challenges the hypotheses that the rates of uptake and volatilisation are always equal in resistant cells and that the plasmid copy number effects replicated by the model are caused by the saturation of MerT in the membrane. Thirdly, the model can guide the design of future experiments. This guidance can minimise the use of laboratory resources and will ensure that sufficient data are created for every parameter in the model under standardised conditions. Finally, the modelling has identified many areas for future biological research: the absolute concentrations of mer proteins, the significance of MerC and MerD, plasmid copy number effects and substrate inhibition, the three uptake processes (non-mer import, MerA transport, and non-MerA transport), the order of DNA + MerR + Hg2+ binding, the nature of toxicity, and the concentrations of mercury in each of the five cellular binding sites. |
| first_indexed | 2025-11-14T19:09:15Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-30521 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:09:15Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-305212025-02-28T13:21:14Z https://eprints.nottingham.ac.uk/30521/ Mathematical modelling of bacterial mercury resistance Crossland, Richard J. A mathematical model of mercury resistance was designed which describes the following reactions: the cellular uptake and volatilisation of Hg2+, binding of the DNA by the regulator, mer protein synthesis, and dilution of quantities by cell growth. A total of 66 biological experiments were then selected from the scientific literature from studies of Tn21 and Tn501 in E. coli at 37 °C. These experiments were repeated in the computer simulation and the information from their 489 data points was incorporated into the 16 parameters of the model using the Metropolis-Hastings algorithm. This model is very useful biology for four reasons. Firstly, it shows whether the data from existing biological experiments are consistent with each other or not. Secondly, it predicts the previously unknown concentrations of mer proteins in cells of each mercury phenotype. In addition, it challenges the hypotheses that the rates of uptake and volatilisation are always equal in resistant cells and that the plasmid copy number effects replicated by the model are caused by the saturation of MerT in the membrane. Thirdly, the model can guide the design of future experiments. This guidance can minimise the use of laboratory resources and will ensure that sufficient data are created for every parameter in the model under standardised conditions. Finally, the modelling has identified many areas for future biological research: the absolute concentrations of mer proteins, the significance of MerC and MerD, plasmid copy number effects and substrate inhibition, the three uptake processes (non-mer import, MerA transport, and non-MerA transport), the order of DNA + MerR + Hg2+ binding, the nature of toxicity, and the concentrations of mercury in each of the five cellular binding sites. 2015-12-10 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/30521/1/RJC_PhD_Corrections_22_10_2015FINAL.pdf Crossland, Richard J. (2015) Mathematical modelling of bacterial mercury resistance. PhD thesis, University of Nottingham. |
| spellingShingle | Crossland, Richard J. Mathematical modelling of bacterial mercury resistance |
| title | Mathematical modelling of bacterial mercury resistance |
| title_full | Mathematical modelling of bacterial mercury resistance |
| title_fullStr | Mathematical modelling of bacterial mercury resistance |
| title_full_unstemmed | Mathematical modelling of bacterial mercury resistance |
| title_short | Mathematical modelling of bacterial mercury resistance |
| title_sort | mathematical modelling of bacterial mercury resistance |
| url | https://eprints.nottingham.ac.uk/30521/ |