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Developing an in vitro Tissue-Engineered Skeletal Muscle Model to Study Ageing and Associated Conditions

  • Full or part time

    Prof C Sale
  • Application Deadline
    Friday, April 12, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The aim of this project is to develop a physiologically relevant cell model of skeletal muscle to investigate ageing, facilitate drug discovery and enhance our understanding of nutritional supplements and exercise.

To achieve this aim, muscle cells from humans will be collected from young and old donors and characterised for the presence of myogenic and ageing markers using molecular techniques such as RNA sequencing and Western blotting. Follow on work will include: i) the development of a 3D tissue engineered muscle to replicate the structure and function of native muscle; ii) culture of the bioengineering muscle in the presence of relevant drugs and nutrients to assess potential changes in myogenic and ageing markers expression, and iii) culture of the bioengineering muscle under mechanically active conditions, mimicking exercise, using to that end a computer controlled bioreactor (Flexcell) to assess potential changes in myogenic and ageing markers.


Applicants must apply using the online form on the University Alliance website at Full details of the programme, eligibility details and a list of available research projects can be seen at

The final deadline for application is 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.

How good is research at Nottingham Trent University in General Engineering?

FTE Category A staff submitted: 14.40

Research output data provided by the Research Excellence Framework (REF)

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