Optimal design for inverse problems in biomedical engineering

生物医学工程中的数学模型往往含有大量的未知参数。这些参数必须通过在每个设置(患者或细胞实验)中单独获取的测量来估计。然而，并不总是清楚关于参数的哪些测量信息最丰富，以及哪些参数与回答给定问题最相关。对于这个“优化设计”的问题，这个项目将通过概率分析和信息论的最新发展来开发一个框架。目标是：1。通过量化从测量中获得的参数信息来开发最优设计框架。在心肌细胞电生理学的电压钳制实验中，开发一种简约表示方案的技术。应用最优设计框架确定识别单个小区参数的最佳方案。将该框架扩展到脉搏波血流动力学的一维模型，以估计单个患者的动脉网络参数。

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.