Mathematical modeling of pancreatic islet behaviour for the improvement of islet transplants in Type 1 Diabetes

Precision Medicine Project - Mathematical modeling of pancreatic islet behaviour for the improvement of islet transplants in Type 1 Diabetes

Supervisor(s): Dr Linus Schumacher & Prof Shereen Forbes
Centre/Institute: Centre for Regenerative Medicine

Background:This project aims to improve the outcomes of islet transplantation through mathematical modelling and computational data analysis.

Islet transplantation is treatment for a subset of patients with Type 1 diabetes (T1D) that involves transplanting insulin-producing islets isolated from a deceased donor pancreas into the liver of a person with Type 1 diabetes. The goal of this procedure is to restore the body’s ability to produce insulin, potentially reducing or eliminating the need for insulin injections and improving blood glucose control. The transplanted islets settle in the liver and begin producing insulin. The benefits are improved glucose regulation, reduced risk of severe hypoglycaemia and a return in awareness of hypoglycaemia in those that have lost the ability to sense hypoglycaemia. Insulin independence may be achieved but there is a gradual attrition in graft function. The benefits of transplant need to be balanced with the risks of immunosuppression including an increased risk of infections and certain cancers.

In research, glucose-stimulated insulin secretion (GSIS) tests are used to measure islet function. First phase insulin secretion (release of preformed insulin granules) has not been considered as a metric for islet health. We have measured first phase insulin secretion in perifusion experiments with collaborators in Edmonton, Canada, as well as data on the outcome of transplantations. We plan to statistically analyse these data to establish whether measuring first phase insulin secretion can improve transplant outcomes.

There are a multitude of factors that might influence the ability of islets to engraft after transplantation. We plan to explore these factors in a mathematical model that simulates the proccess of how islets move through and settle into the liver from the transplant site (e.g. portal vein). Input parameters will include (but not be restricted to) islet size distribution and number of transplanted islets. 3D images of cleared livers (in collaboration with Novo Nordisk) will be used to determine a simulation gemeotry representative of rodent liver anatomy.

Aims

  1. Quantify islet perifusion data on first phase insulin secretion and mathematically model the relationship to transplant outcomes (C-peptide and glucose measurements at 4 weeks and 1 year) taking into account other factors (eg. donor age, ischaemic time, islet numbers, recipient age, sex, immunosuppression)
  2. Establish a mathematical modelling framework for islet transplants. The mathematical model would be informed by experimental data and make predictions about which factors could improve islet distribution and/or engraftment. The mathematical model will be kept as simple as possible (for computational efficiency and interpretability) and as complex as necessary to achieve biomedically meaningful representation
  3. Compare outputs of the mathematical model against distribution of islets in experimental 3D images from rodent models to constrain model parameters using statistical inference techniques (such as simulator-based Bayesian inference or surrogate-based machine learning methods)
  4. Use the parameterized model to predict islet distribution after transplantation in human livers, taking into account known differences in anatomy from rodent to human (and difference in transplantation parameters, such as number of transplanted islets).
  5. Screen in silico for factors that might improve the distribution and/or number of engrafted islets

Training outcomes

This project is a good springboard for a student to go into interdisciplinary or biomoedical research in academia or industry. The Centre for Regenerative Medicine provides a stimulating research environment and training opportunities for PhD students. The Schumacher group has extensive experience in building mathematical models and computational data analysis.

This project is ambitious and novel, as no-one has published mathematical models of device-less islet transplantation, but also feasible as models can be based on well-established cell and tissue simulation frameworks (such as PhysiCell [4] or Chaste).

References

  1. Benninger, Piston, Trends Endocrinol Metab, 2014. 25(8): p. 399-406. 
  2. Vierra, Jacobson, Mol Metab, 2018. 9: p. 84-97. 
  3. Vierra, Jacobson, Sci Signal, 2017. 10(497).
  4. Ghaffarizadeh et al., PLoS Comput. Biol. 14(2): e1005991, 2018. 

Apply Now

Click here to Apply Now

  • The deadline for 25/26 applications is Monday 13th January 2025
  • Applicants must apply to a specific project. Please ensure you include details of the project on the Recruitment Form below, which you must submit to the research proposal section of your EUCLID application. 
  • Please ensure you upload as many of the requested documents as possible, including a CV, at the time of submitting your EUCLID application.  

 

Q&A Sessions

Supervisor(s) of each project will be holding a 30 minute Q&A session in the first two weeks of December. 

If you have any questions regarding this project, you are invited to attend the session on Tuesday 3rd December at 2pm GMT via Microsoft Teams. Click here (link to follow) to join the session.

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