Multi-systems modelling approaches to healthy ageing

Strategic relevance:

Healthy ageing is essential to prevent overburdening of health care facilities and to capture the potential of an increasingly older population. Since 2001, commitments to achieving healthy ageing have featured heavily in government policy and directives. However, over 15 years later healthy ageing for all remains elusive. To deliver the goal of increasing healthy, active ageing, we must first understand what causes ageing, if we are to develop means to influence the processes.

Scientific excellence:

We propose to develop predictive in silico models of muscle wasting across the life span as a result of identifying key determinants of muscular plasticity with age. A novel, world-leading multidisciplinary approach combining demographic, experimental cellular and biological data with computer simulations, to determine, manipulate and model ageing health will underpin our research.

Clear aim and hypothesis:

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.

Interdisciplinarity and fit with relevant DTA programme:

A highly skilled, interdisciplinary team will provide a resource of biological data combined with in silico predictive modelling and machine learning/pattern recognition analysis, which will influence the way we support lifelong health and wellbeing.

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 Monday 8 October 2018. There will be another opportunity to apply for DTA3 projects in the spring of 2019. The list of available projects is likely to change for the second intake.