Optimal design for inverse problems in biomedical engineering

Mathematical models in biomedical engineering often have a large number of unknown parameters. These parameters must be estimated through measurements acquired individually in each setup (patient or cell experiment). However, it is not always clear which measurements are most informative about the parameters and which parameters are most relevant to answer a given question. For this problem of 'optimal design', this project will develop a framework through recent developments in probabilistic analysis and information theory. The objectives are:

1. To develop an optimal design framework by quantifying the parameter information gain from the measurements.
2. Develop a technique for parsimonious representation of protocols in voltage-clamped experiments of cardiac cell electrophysiology.
3. Apply the optimal design framework to determine the best protocols for identification of individual cell parameters.
4. Extend the framework to 1D models of pulse wave haemodynamics for estimating individual patient arterial network parameters.