About the Project
This is an applied computational biophysical modelling PhD, suited to candidates with a biomedical engineering, applied mathematics, computer science, physics or equivalent undergraduate degree. The project will investigate, through simulation, the mechanisms causing late term stillbirths in pregnant woman suffering intrahepatic cholestasis of pregnancy (ICP). During ICP the liver fails to metabolise digestive fluid (bile), which can impede liver function and cause the mother’s blood concentrations of bile acids to rise. This affects 1 in 140 pregnant women. It is also characterised by raised serum concentrations of bile acids in the fetus and is associated with an increased rate of unexplained stillbirth, often occurring late in pregnancy. The exact cause of stillbirth within this group of women is not fully understood, but emerging evidence implicates bile acid-induced cardiac arrhythmia as a likely cause.
During pregnancy in vitro studies show that the raised bile acids in the fetal circulation can bind to cardiac myocyte proteins causing abnormal contraction, calcium dynamics and propagation of the electrical activation wave. These changes may explain the increased arrhythmogenic risk in the fetal heart. In this project we will use computational biophysical modelling to link these in vitro results with in vivo clinical measurements.
This project will use computational models of cardiac cellular and organ physiology to integrate clinical and basic science observations to answer an important clinical question. The project will involve developing the first model of human fetus cardiac myocyte electrophysiology, developing three dimensional model of the fetus heart from medical imaging and simulating the effects of bile acids on cellular and organ electrophysiology to predict arrhythmogenic risk and identify biomarkers that could identify at risk fetuses.
The project will require the numerical solution of systems of non-linear ordinary differential equations (ODEs) and partial differential equations for cellular and organ scale models. Candidates are preferred who have experience with the finite element method, stiff ODE solvers, high level programming (e.g. C/C++, Fortran), scripting languages (e.g. Python, Perl), machine learning techniques, Bayesian parameter inference, non-linear optimisation, image processing, analysing biological and clinical data sets and working in a multi-disciplinary environment.
Candidates are requested to provide:
+ A curriculum vitae
+ A statement (up to 1000 words) summarising why they want to do this project, what they would like to get out of this project and what makes them the best candidate for the position.
+ An example of a document that they have authored (a publication, extended conference abstract, undergraduate / masters thesis).
+ Two referees who could be contacted following an interview.
Please email these to Dr Steven Niederer at [Email Address Removed]
The deadline for this project is the 28th May 2018
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