EPSRC Reference: |
EP/W000490/1 |
Title: |
Integrating data-driven biophysical models into respiratory medicine - BIOREME |
Principal Investigator: |
Brook, Professor B |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Sch of Mathematical Sciences |
Organisation: |
University of Nottingham |
Scheme: |
Standard Research - NR1 |
Starts: |
01 January 2022 |
Ends: |
31 December 2025 |
Value (£): |
763,404
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EPSRC Research Topic Classifications: |
Medical Imaging |
Modelling & simul. of IT sys. |
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EPSRC Industrial Sector Classifications: |
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Related Grants: |
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Panel History: |
Panel Date | Panel Name | Outcome |
17 Mar 2021
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HT New Challenges NetworkPlus Interview Panel
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Announced
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Summary on Grant Application Form |
Lung diseases such as Asthma and Chronic Obstructive Pulmonary Disease affect one in five people in the UK and kill someone every 5 minutes. The number of patients with these lung diseases was increasing in the NHS even before COVID-19. We are also learning about serious long-term effects of COVID-19 that will add to the existing burden on the NHS.
There have been huge advances in technologies that allow scientists to see inside the lungs and measure what we breathe out. While this information has taught us quite a lot, it is still very difficult to combine different sources of information and turn it into new or improved treatments. Getting that useful information out of large amounts of medical test results requires sophisticated physics-based mathematical and statistical models run on powerful computers - a combination of techniques called data-driven biophysical multiscale modelling. The ability to develop those kinds of models will allow us to better understand how diseases start and how they progress.
Our BIOREME network will support new research that uses these techniques to mimic biological and mechanical processes that occur throughout the lung. Using the information from thousands of lung tests, the idea is then to get these models to mimic real diseased lungs. In order to improve and build trust in these models, some of our projects will be focused on comparing their outputs to results from other lung tests. Medical scientists can then use such models to test what might happen in a particular type of lung disease, and to investigate possible responses to new treatments before testing these in patients. Most importantly, this will lead to the design of new drugs and improved trials for new treatments.
The first step will be to get medics, imaging experts and mathematicians together with industry and patient group representatives to decide on which specific research areas to prioritise, where this form of modelling will make the most difference. This NetworkPlus award will then allow us to organise multiple events, in different formats, designed to help researchers to collaborate, and to come up with the best initial projects to help achieve our goals. We will then help the researchers to develop these into larger projects that will attract funding from other sources and continue the research into the future. Even after this funding runs out, BIOREME will provide a lively forum for lung researchers to continue solving problems using these advanced computational tools. Finally, BIOREME will support outreach activities to engage and educate communities and young people in the role that mathematics can play in medicine and healthcare, and to inspire a new generation of respiratory scientists from diverse backgrounds.
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Key Findings |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.nottingham.ac.uk |