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Details of Grant 

EPSRC Reference: EP/N014499/1
Title: EPSRC Centre for New Mathematical Sciences Capabilities for Healthcare Technologies
Principal Investigator: Chen, Professor K
Other Investigators:
Czanner, Dr G O Brien, Professor SJ Movchan, Professor A
Karpenkov, Dr O Movchan, Professor N Bearon, Dr RN
Vasiev, Dr BN Williamson, Professor P Falciani, Professor F
Weightman, Professor P See, Dr V Read, Dr J
Garcia-Finana (van der Hoek), Dr M Sharkey, Dr KJ Barrett, Dr S
Horsburgh, Dr MJ Pratoussevitch, Dr A Webb, Dr SD
Elsheikh, Professor A Kolamunnage-Dona, Dr R
Researcher Co-Investigators:
Project Partners:
AstraZeneca Bionow Ltd Carl Zeiss
Clatterbridge Cancer Ctr NHS Fdn Trust Dudley Group of Hospitals NHS Trust Durham, University of
Liverpool Health Partners Liverpool Heart & Chest Hosp NHS Trust Liverpool Women's Hospital
Mirada Medical UK North West Coast Academic Health Sci Nwk Public Health England
Royal Liverpool University NHS Hospitals Unilever University of Edinburgh
University of Liverpool University of Salford Walton Centre Neurology/Neurosurgery
Department: Mathematical Sciences
Organisation: University of Liverpool
Scheme: Standard Research
Starts: 01 December 2015 Ends: 30 November 2019 Value (£): 2,004,298
EPSRC Research Topic Classifications:
Complexity Science Med.Instrument.Device& Equip.
Medical science & disease Non-linear Systems Mathematics
Numerical Analysis
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
09 Sep 2015 Maths in Healthcare - Interviews Announced
Summary on Grant Application Form
As quality of life constantly improves, the average lifespan will continue to increase. Underlining this improvement is the vast amount of the UK government's support to NHS (£133.5 billion in year 2011/12) and the UK pharmaceutical industry's R&D large investment (4.9 billion to R&D in year 2011/12). The expectation of quality healthcare is inevitably high from all stakeholders. Fortunately recent advances in science and technology have enabled us to work towards personalised medicine and preventative care. This approach calls for a collective effort of researchers from a vast spectrum of specialised subjects.

Advances in science and engineering is often accompanied by major development of mathematical sciences, as the latter underpin all other sciences. The UoL Centre will consist of a large and multidisciplinary team of applied and pure mathematicians, and statisticians together with healthcare researchers, clinicians and industrialists, collaborating with 15 HEIs and 40 NHS trusts plus other industrial partners and including our strongest groups:

MRC Centre in Drug Safety Science, Centre for Cell imaging (CCI for live 3D and 4D imaging), Centre for Mathematical Imaging Techniques (unique in UK), Liverpool Biomedical EM unit, MRC Regenerative Medicine Hub, NIHR Health Protection Research Units, MRC Hub for Trials Methodology Research.

Several research themes are highlighted below:

Firstly, an improved understanding of the interaction dynamics of cells and tissues is crucial to developing effective future cures for cancer. Much of the current work is in 2D, with restrictive assumptions and without access to real data for modelling. We shall use the unparalleled real data of cell interactions in a 3D setting, generated at UoL's CCI. The real-life images obtained will have low contrast and noise and they will be analysed and enhanced by our imaging team through developing accurate and high resolution imaging models. The main imaging tools needed are segmentaton methods (identifying objects such as cells and tissues regions in terms of sizes, shapes and precise boundaries). We shall propose and study a class of new 3D models, using our imaging data and analysis tools, to investigate and predict the spatial-temporal dynamics.

Secondly, better models of how drugs are delivered to cells in tissues will improve personalised predictions of drug toxicity. We shall combine novel-imaging data of drug penetration into 3D experimental model systems with multi-scale mathematical models which scale-up from the level of cells to these model systems, with the ultimate aim of making better in-vitro to in-vivo predictions.

Thirdly, there exist many competing models and software for imaging processing. However, for real images that have noise and are of low contrast, few methods are robust and accurate. To improve the modelling, applied and pure mathematicians team up to consider using more sophisticated tools of hyperbolic geometry and Riemann surfaces and fractional calculus to meet the demand for accuracy, and, applied mathematicians and statisticians will team up to design better data fidelity terms to model image discrepancies.

Fourthly, resistance to current antibiotics means that previously treatable diseases are becoming deadly again. To understand and mitigate this, a better understanding is needed for how this resistance builds up across the human interaction networks and how it depends on antibiotic prescribing practices. To understand these scenarios, the mathematics competition in heterogeneous environments needs to be better understood. Our team links mathematical experts in analysing dynamical systems with experts in antimicrobial resistance and GPs to determine strategies that will mitigate or slow the development of anti-microbial resistance.

Our research themes are aligned with, and will add value to, existing and current UoL and Research Council strategic investments, activities and future plans.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.liv.ac.uk