Mechanobiology is the study of how cells sense and respond to physical forces. Mechanical signals from the environment and physical communication between cells are critical for normal development, wound healing, and disease pathology. Cells transmit mechanical signals through the extracellular matrix, adhesion receptors, the cytoskeleton, and the nuclear lamina to give rise to changes in behaviour such as migration, proliferation, and differentiation. Understanding how cells process mechanical information has broad therapeutic applications, from cancer therapy to tissue engineering. My group is interested in how cells transduce mechanical signals into changes in gene expression during wound healing. This work is directly relevant for assessing the biosafety of medical implants and predicting the efficacy of drugs on cells in different microenvironments.
The core methodology we use is high content microscopy and automated image analysis, which allows us to measure hundreds of parameters in thousands or even millions of individual cells. Statistical models and machine learning are then applied to these big datasets to infer relationships between variables and to generate hypotheses, which are tested in the lab. Computation and experimentation thus form an iterative research process.
This PhD project will focus on the dynamic regulation of mechanically-sensitive regulators of gene transcription that are involved in wound healing as a function of cell mechanics and cell-surface interactions.
Candidates should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent qualification gained outside the UK) in a relevant subject. A master’s level qualification would also be advantageous.
In addition, candidates should have some background in biostatistics, coding (e.g. Python), and/or computer vision, as well as basic cell biology. Training will include key techniques in animal cell culture, optical microscopy, live cell imaging, molecular biology, gene and protein expression analysis, and collaboration with groups in mathematics and computer science at Bath University.
Informal enquiries should be directed to Dr Julia Sero, [email protected]
Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUBB-FP02&code2=0013
Please ensure that you quote the supervisor’s name and project title in the ‘Your research interests’ section.
More information about applying for a PhD at Bath may be found here: http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/
Anticipated start date: 30 September 2019.
Key words: mechanobiology, cell signalling, microscopy, systems biology, machine learning, biostatistics, tissue engineering, computational modelling, cancer, cell adhesion, biomaterials