Robust cardiac function depends on the billions of individual cells that make up the heart working together to contract synchronously and pump blood. While all cardiac cells are similar, experimental measurements show that each cell is also distinct. Conventionally experimental and clinical recordings are averaged to remove variability from measurements, however, this approach removes the measurement of potentially important physiological or pathological co-variation and variation. Combining advanced multi-scale biophysical computer simulations of cardiac function (Figure 1) with Bayesian statistical approaches, this PhD will develop a statistical-systems approach to quantify the degree and impact of variability and covariance in clinical and experimental measurements.
This PhD Project will involve the numerical simulation of cardiac function using systems of non-linear ordinary and partial differential equations solved using the finite element method, the development and application of efficient Markov Chain Monte Carlo sampling methods for high dimensional biological applications, the use of hierarchical Bayesian modelling to quantify population statistics, forward simulations to quantify uncertainty in model predictions and analysis of the physiological and pathological impacts of protein variability and co-variability. The project is inter-disciplinary will involve working with experimental and clinical data, collaborating with biologists and cardiologists, and using models to optimise experimental design and sampling. The project would suite a candidate with a background in statistics, applied mathematics, engineering, physics or computer science.