Modelling the alternative splicing of tissue growth regulators and its implications for tumour growth

Modelling the alternative splicing of tissue growth regulators and its implications for tumour growth
Modelling and Analytics for Medicine and Life sciences Doctoral Training Centre: PhD Scholarship

Supervisors: Professor Markus Owen (School of Mathematical Sciences), Professor David Bates (Division of Cancer and Stem Cells, School of Medicine)

Project description:
Normal and pathological tissue growth is regulated by diverse growth factors and related molecules, many of which are produced in cells via the transcription of associated genes and translation of mRNA to protein. In many cases, alternative splicing, regulated by splicing factors, leads to different isoforms of proteins, which can have different effects. This is particularly pertinent to angiogenesis, the process whereby new blood vessels are produced from existing ones, which is crucial in cancer and also diseases such as diabetic retinopathy.

Different isoforms of Vascular Endothelial Growth Factor (VEGF), whose balance is regulated by alternative splicing, can promote or inhibit angiogenesis. In fact, the relevant splicing factors seem to regulate alternative splicing of families of genes controlling cell death, growth factor signaling, the cell cycle, invasion and immune responses. Thus it important to consider the overall effect of splicing factors in the context of a whole tissue where all these processes are modulated.

This project will focus on mathematical modelling of the various aspects of alternative growth factor splicing, regulation of angiogenesis, and tumour growth, with the following objectives:
1. model splicing control at network level;
2. model the implications for tissue growth of altered splicing control
3. couple O1 and O2 to predict the efficacy of interventions that modulate alternative splicing in cancer.
This will require the develop and application of advanced mathematical and computational techniques to make the link from molecules to cells to tissues. A significant challenge is to use a blend of mathematical and statistical approaches to allow the translation of varied experimental data and knowledge into tractable parameterised mathematical frameworks that combine dynamics over a range of scales.

This project would also involve co-operation with Exonate, a biopharmaceutical company focussed on the discovery and development of small molecule drugs that modulate alternative mRNA splicing to address diseases of high unmet medical need. Exonate will provide relevant data and scientific input, and also contribute to the student training, for example by through hosting them within the company on secondment.

References
M R Owen et al. Cancer Res 71(8) 2826-37 (2011)

The MAML programme: The MAML doctoral training programme focuses on innovative modelling, simulation and data analysis to study real-world problems in medicine and biology. Maintaining a healthy society creates major challenges in areas including ageing, cancer, drug resistance, chronic disease and mental health. Addressing such challenges necessitates continuing development and implementation of a raft of new mathematical approaches and their integration with experimental and clinical science. Students will apply mathematical approaches (from areas such as dynamic modelling, informatics, network theory, scientific computation and uncertainty quantification) to research projects at the forefront of biomedical and life sciences identified through well-established collaborations with both academic and industrial partners.

MAML students will be provided with an excellent training environment within the Centre for Mathematical Medicine and Biology and collaborating departments. Students will undertake tailored training, complemented by broadening, soft-skills, wet-lab (where appropriate) and student-led activities. There will also be opportunities for training and exchanges with world-leading partners.

Summary: These 3.5 year PhD scholarships start in September 2018. Successful applicants will receive a stipend (£14,553 per annum for 2017/8) for up to 3.5 years, tuition fees and a Research Training Support Grant. Fully funded studentships are available for UK applicants. EU applicants who are able to confirm that they have been resident in the UK for a minimum of 3 years prior to the start date of the programme may be eligible for a full award, and may apply for a fees-only award otherwise

Applications: Please follow the instructions at the MAML website: http://www.nottingham.ac.uk/mathematics/maml Applicants for the MAML programme should have at least a 2:1 degree in mathematics, statistics or a similarly quantitative discipline (such as physics, engineering, or computer science).

Completed applications and references should be submitted by Wednesday 28 February 2018.


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