Mathematical modelling of fatty liver disease

Mathematical modelling of fatty liver disease
Modelling and Analytics for Medicine and Life sciences Doctoral Training Centre: PhD Scholarship

Supervisors: Professor Jonathan Wattis (Maths) and Professor Andy Salter (Biosciences).

Project description: The aim of this project is to produce detailed models of certain fat metabolism pathways in the liver. Metabolic Syndrome (MetSyn) represents a group of metabolic abnormalities associated with insulin resistance and leading to increased risk of developing cardiovascular disease (CVD) and type 2 diabetes. A further consequence of MetSyn is accumulation of lipid inside liver cells (Non-alcoholic Fatty Liver Disease , NAFLD) which can ultimately lead to cirrhosis, cancer or liver failure.

A greater understanding of the pathophysiology of MetSyn should help provide potential interventions to prevent NAFLD and more serious liver degeneration. There are many pathways which influence hepatic lipid accumulation. Existing models of fluxes through these pathways in the fasted and fed states have been fitted to healthy subjects, as well as a range of patients with varying degrees of insulin resistance. However, the longer term influences of the amount and type of food consumed on the accumulation of liver fat remain to be effectively modelled.

We will start with the production of VLDL in the liver and the role of specific enzymes , including stearoyl coenzyme A desaturase (SCD) and diacylglycerol acyltransferase enzymes (DGAT) . SCD regulates the conversation of saturated to monounsaturated fatty acids and appears critical to the subsequent formation of triacylglycerol (TAG). DGAT are involved in the synthesis of TAG from diacylgycerol in the cytosol and endoplasmic reticulum, and the relative activity of different isoforms may be a key factor in whether TAG is stored intracellularly or secreted within VLDL. Other areas where more detailed modelling is required is the breakdown of chylomicrons into remnant particles and the delivery of dietary TAG to the liver within these remnants. This means that ingested fats are adsorbed slowly and give rise to an input of fat over a prolonged period after eating. As these particles are digested their size decreases and their composition also changes, as TAG and cholesterol are removed separately. A main challenge in the project is to convert results on the dynamics which occur between one meal and the next into an understanding of the longer timescale of decades over which fat accumulates in the liver. Thus we will aim to investigate the effects of allowing certain parameters to vary slowly over various significantly longer timescales. Such modelling may help to identify key points in the development of NAFLD and suggest potential nutritional/pharmaceutical interventions to slow, or even reverse, the process.

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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