Prof C Stewart, Dr S Webb, Dr S Ortega Martorell, Dr R Foster
No more applications being accepted
Funded PhD Project (Students Worldwide)
About the Project
Our overarching aim is to improve lifelong health and well being, informed by in vivo, in vitro and associated large data modelling. Key hypotheses are: 1. in order to improve lifelong health, we need to scrutinise biological adaptations across the lifecourse and in so doing we will identify biomarkers of health/ill health, influenced by activity/inactivity, age and gender; 2. local cues in muscle cells will be as important as systemic regulators in influencing muscle mass and therefore function; 3. network evolutionary in silico modelling of in vivo and in vitro data from this unique cohort will enable correlation and manipulation of adaptive strategies of health.
Methodology and innovations
Following recruitment, a relevant demographic profile of active or sedentary males/females, ages 20-80 (grouped: 20-35, 36-50, 51-65, 65-80 for recruitment purposes), will be undertaken, underpinned by anthropometry (height, wait, BMI), physiology (DXA, strength, function, metabolism), nutrition and health (questionnaires), muscle biopsies, muscle stem cells and blood sampling. Initial in vivo profiling will enable achievement of the following objectives: 1. Determine and correlate the impact of age, gender, exercise and health on adaptive phenotypes of muscle stem cell growth (FLOW cytometry), fusion (Morphology, PCR and creatine kinase activity), metabolism (ELISA, FLOW and Western blotting), repair and survival (Live imaging). 2. Build on objective 1, to interrogate metabolomic biomarkers (1D nuclear magnetic resonance (NMR) spectra optimised for metabolomics, acquired using the 1D NOESY pulse sequence) of health in cells, serum and tissue. 3. Use co-culture studies of old or young human skeletal muscle cells with young or old serum/proteins, respectively to manipulate and interrogate intrinsic (muscle) vs. endocrine regulators of change. 4. Instruct development of a network in silico modelling and pattern recognition/machine learning using the human, cellular and metabolomic data to forecast, simulate and positively inform healthy, independent older age.
Applications
Applicants must apply using the online form on the University Alliance website at https://unialliance.ac.uk/dta/cofund/how-to-apply/. Full details of the programme, eligibility details and a list of available research projects can be seen at https://unialliance.ac.uk/dta/cofund/
The final deadline for application is Friday 12 April 2019.
Funding Notes
DTA3/COFUND participants will be employed for 36 months with a minimum salary of (approximately) £20,989 per annum. Tuition fees will waived for DTA3/COFUND participants who will also be able to access an annual DTA elective bursary to enable attendance at DTA training events and interact with colleagues across the Doctoral Training Alliance(s).
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801604.
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