This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP) for entry in October 2023. The GW4+ DTP consists of the Great Western Four alliance of the Universities of Bath, Bristol and Exeter and Cardiff University plus five prestigious Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology & Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad multi-disciplinary training, designed to produce tomorrow’s leaders in earth and environmental science.
Supervisory Team:
Lead Supervisor: Dr Matthew Grayson, Department of Chemistry, University of Bath
Co-Supervisor: Prof. Varinder Aggarwal, School of Chemistry, University of Bristol
Co-Supervisor: Dr Lee Bryant, Department of Architecture & Civil Engineering, University of Bath
Project Background
Global industrialization has resulted in organic pollutants entering aquatic environments. Therefore, a detailed understanding of the long-term availability and fate of pollutants and what impact they have on aquatic life is needed. This requires knowledge of the transport mechanisms by which organic compounds enter aquatic environments and how they then interact with organisms.
Animal testing has traditionally been used to assess the safety of chemicals. However, more sustainable approaches to safety testing are required due to the ethical concerns, costs and time scales associated with in vivo methods. To reduce the number of animals used in toxicity testing, new approaches are required that can assess the potential of organic compounds to cause harm to aquatic life. For such compounds, chemical reactivity contributes significantly towards their toxicological profile through covalent modification of biological nucleophiles. Our previous work led to the development of a fast, computational method for assessing the mutagenic risk of pharmaceutically important organic electrophiles (J. Chem. Inf. Model. 2019, 59, 5099). DFT-derived LUMO energies and activation barriers for reaction between a model nucleobase and electrophiles showed significant predictivity for the assessment of mutagenic potential. However, DFT calculations are time-consuming and expert-technical knowledge is required to perform them.
Project Aims and Methods
In this project, machine learning (ML) models will be developed that can, once trained, rapidly and easily predict reactivity descriptors for use in the prediction of aquatic toxicity. This work will lead to a new ML protocol for computationally assessing the toxicity of pollutants and understanding what impact they will have on aquatic life. Collaboration with Prof. Aggarwal, as a co-supervisor for the project, will focus on validating the new in silico prediction models. Predictions about the reactivity of novel electrophiles will be made using these models which will then be tested in the Aggarwal lab at Bristol. Close agreement between the computationally predicted and experimentally determined reactivity for this external test set will provide confidence in using these predictive models in chemical risk assessment. Collaboration with Dr Bryant, as a co-supervisor for the project, will focus on the mass-transport mechanisms by which organic compounds enter aquatic environments on catchment and system scales, using a local drinking-water-supply reservoir as a project study site. Dr Bryant will also support the investigation of the transport and distribution of organic compounds within the water and sediment components of an aquatic system.
The ML and synthesis work will provide insight into mechanism-based toxicity. The work with Dr Bryant will examine exposure mechanisms. Overall, this will provide a holistic approach to understanding the long-term impact of pollutants on aquatic life.
The supervisory team are happy to adapt the project to better match the interests of the student. Please contact Dr Grayson to discuss this further.
Candidate requirements
We are looking for a highly motivated individual to join our groups. Experience with coding (any language) is desirable but not essential. Experience with machine learning, reaction modelling and synthesis is not essential.
Applicants must have obtained, or be about to obtain, a UK Honours degree at 1st or 2.1 level, or international equivalent.
Non-UK applicants must meet the programme’s English language requirement by 01 February 2023 (the only exemption is if you will be awarded a UK degree or degree conducted in English before your PhD start date).
Training
As a PhD student, you will:
• Gain experience in the use of machine learning, reaction modelling and synthesis applied to toxicology.
• Perform both individual and collaborative research projects.
• Write up research results for publication in scientific journals.
• Disseminate your work through presentations at national and international conferences.
Enquiries and Applications:
Informal enquiries are welcomed and should be directed to Dr Matthew Grayson, [Email Address Removed]
Formal applications should be made via the University of Bath's online application form for a PhD in Chemistry.
When completing the form, please identify your application as being for the NERC GW4+ DTP studentship competition in Section 3 Finance (question 2) and quote the project title and lead supervisor’s name in the ‘Your research interests’ section.
More information about applying for a PhD at Bath may be found on our website.
We welcome and encourage student applications from under-represented groups. We value a diverse research environment. If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
Project keywords: Ecotoxicology (Biological Sciences), Environmental Chemistry (Chemistry), Machine Learning (Computer science), Synthetic Chemistry (Chemistry), Organic Chemistry (Chemistry), Computational Chemistry (Chemistry), Toxicology (Medicine), Pollution (Environmental Sciences).
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