University of Exeter

Country: United Kingdom
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  • Funder: UKRI Project Code: EP/P51049X/1
    Funder Contribution: 91,276 GBP
    Partners: University of Exeter

    Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

  • Funder: UKRI Project Code: EP/N017846/1
    Funder Contribution: 599,738 GBP
    Partners: University of Exeter

    In recent years, advances in both the physical and life sciences have increasingly come from the collaborations of researchers across disciplines, and the development and use of tools from a range of areas. A prototypical example of this interdisciplinary approach to science is systems biology, the field concerned with quantifying how the interaction of individual system components control biological function and behaviour. Systems biology has become increasingly quantitative, with a shift from diagrammatic representations of interaction networks to sets of mathematical equations that model (i.e. simulate) how the concentrations of molecular species vary with time. A key advantage of such models is that they can be used to predict how the networks they represent will respond to specific perturbations, such as changes in environmental conditions (e.g. temperature) or the addition of pharmacological agents. The ability to easily generate such predictions reduces the need for large numbers of expensive and time-consuming experiments. However, the more complex a biological network is, the more complex the corresponding model needs to be, and the greater the range of possible biological behaviours that can be exhibited. This means that extensive computer simulations are needed to adjust the parameters controlling the model so as to accurately reproduce (i.e. fit) the experimental behaviour observed. For biologically realistic models which can involve hundreds of different molecular species, the number of simulations required to adjust the parameters of a given model to achieve the optimal fit to data can be prohibitively large, far exceeding that which is possible on practical timescales. Thus, for the predictive power of mathematical models to be fully realised in the systems biology domain, methods are required that allow this parameter optimisation procedure to be carried out in a computationally efficient manner. The proposed project will address this need by bringing state-of-the-art methods from computer science to bear on the problem, which have been successfully applied previously to highly parametrised problems like aircraft conflict alert systems, design optimisation of lightweight materials and routing of mesh sensor networks (amongst others). In addition, we propose to develop new methods specifically engineered for the systems biology domain that can provide insight into model behaviour, beyond simply returning a single estimate of the best fit parametrisation (e.g. methods for identifying parameters yielding equally good fits to data, and also parameters which simultaneously fit the model to data generated in diverse experimental conditions). As part of this, we will develop a package of open source software tools that will be embedded within a software infrastructure designed for systems biologists, enabling the methods developed in this work to be readily applied to problems in the field that are currently computationally intractable. To test and refine the algorithms developed, they will be applied to the gene network that generates circadian oscillations (the circadian clock) in the key plant species Arabidopsis thaliana, for which high-quality experimental data recorded in a range of genetic and environmental backgrounds is available, together with a suite of mathematical models of varying complexity. As part of this work, biochemically detailed models of the clock will be directly fitted to multiple experimental datasets for the first time, yielding models with greater predictive power. Many processes critical for plant growth and reproduction are regulated by the clock (e.g. photosynthesis and flowering time). In the long term, the ability to optimise plant models of increasing complexity with the class of methods we will develop here may thus help predict how the viability of economically important crop species will be affected by future temperature shifts resulting from climate change.

  • Project . 2006 - 2009
    Funder: UKRI Project Code: NE/E52303X/1
    Funder Contribution: 203,699 GBP
    Partners: University of Exeter

    The MSc Mining Geology course is intensive. It provides advanced knowledge and training in the formation, discovery, extraction, processing, environmental impact and management of metalliferous ores and industrial mineral (including construction materials) resources. This grant supports five full studentships for three years.

  • Funder: UKRI Project Code: NE/K005650/1
    Funder Contribution: 379,603 GBP
    Partners: University of Exeter

    The current high rates of species extinctions are well publicised and it is clear that much of this is due to human activities such as overexploitation, habitat destruction and global environmental change due to greenhouse gas emissions. The loss of species is not just a moral and aesthetic issue. It has been demonstrated repeatedly that the ecosystem services (food production, soaking up of carbon emissions, pest control, flood control etc.) that human societies rely on are positively related to species diversity in ecosystems. The protection of biodiversity is therefore a major priority for governments. The effectiveness of this protection depends on how well we understand the processes that lead to species declines. Sometimes this is obvious: The collapse of cod stocks is clearly due to overfishing. Often however, such direct impacts are followed by secondary extinctions of other species, for not always obvious reasons, with the danger that this leads to a cascade of further extinctions and ecosystem collapse. Predicting these cascades is challenging and requires a detailed understanding of how the interconnectedness of species in ecosystems affects the transmission of human impacts on one species to other species that are not directly linked to it. This is particularly important for species at higher trophic levels (carnivores) which are most vulnerable to extinction. The idea has long existed that species of carnivore that specialise on different prey have positive effects on each other by limiting their prey populations and thereby preventing one prey species from outcompeting the other. A consequence of this is that if a carnivore is lost from the community, its prey is released from control and may subsequently out-compete the other prey species leaving the other carnivore without food and facing extinction. This is potentially an important mechanism by which extinction cascades occur, however, it is difficult to obtain experimental evidence for such effects. We have done preliminary experiments with simple insect communities in the laboratory which have demonstrated that the removal of one carnivore species does indeed lead to the extinction of others in just a few generations. The challenge now is to scale this model system up to a more realistic scale. We propose to carry out experiments in field-based mesocosms - roughly 2 meter cubed enclosures in which we can control the exact composition of the ecological community. We will assemble communities of insects in these and impose specific harvesting regimes on target species and follow the indirect impact this has on the other species and, in particular, whether the predicted extinction cascades occur. Within this setting we can manipulate important variables, such as the strength of competition among prey species, and test their impact on the outcome. We can also test the prediction that more species-rich and complex communities will be more resistant to extinction cascades, which would mean that as biodiversity is lost, the chance that further losses trigger extinction cascades increases.

  • Funder: UKRI Project Code: 2596016
    Partners: University of Exeter

    Insufficient UK climate change policy (IPPR 2020) means individual and household behaviour change alone cannot achieve the significant system change needed to address the climate and ecological crisis (IPCC 2018). Individuals must also take political action (defined as action to change systems beyond the individual or household). A majority of the UK public consider radical change necessary to mitigate climate change, but most are not acting for such change. This innovative interdisciplinary project aims to demonstrate positive future visioning as a novel method of closing this prevalent value-action gap. I combine literatures on transformative change and wellbeing. The research contributes to UK policy focus on climate change adaptation and mitigation, and wellbeing of future generations. Research questions Main question: does envisioning positive futures support environmental political action? Sub-questions: - How does such envisioning increase understanding, motivation, and agency in enacting social transformations for sustainability? - What is the relative importance in this of deliberative methodologies and emotional reflection? Methodology My qualitative experimental methodology involves an intervention engaging two diverse groups of 10 people in developing a future vision around a question like 'how could life look in 30 years if we had a sustainable local area and UK, with true wellbeing for everyone?'. They will be compared to a no-intervention control group. Small groups are effective in transformative change. I will recruit participants using random stratified selection from respondents to an online questionnaire advertised via community groups, local businesses and social media in several locations in Devon and Cornwall. I will adhere to GDPR and principles of anonymisation, confidentiality and informed consent.