Mathematical models are often used to describe and, in some cases predict, excitable cellular behaviour that is based on observed experimental results. With the increase of computational power, it is now possible to solve such models in a relatively short time. This, along with an increasing knowledge of cellular and subcellular processes, has led to the development of a large number of complex cellular models, capable of describing a broad range of excitable cell behaviour. But the use of complex models can also lead to problems. Most models can accurately reproduce results associated with the data on which the models are based. However, results from complicated models, with large numbers of variables and parameters, are less reliable if the model is not placed under the same physiological conditions as defined by the model author. In order to test a model??s suitability and robustness over a range of physiological conditions, one needs to fit model parameters against experimental data observed under those conditions. By using the modelling standard and repository offered by CellML, model users can easily select and adapt a large number of models to set up their own applications to fit model parameters against user-supplied experimental data. However, currently there is a lack of software that can utilise CellML model for parameter fitting. In this thesis, a Java-based utility has been developed, capable of performing least square parameter optimisation for a wide range of CellML models. Using the developed software, a number of parameter fits and identifiability analyses were performed on a selected group of CellML models. It was found that most of the models were ill-formed, with larger numbers of parameters worsening model identifiability. In some cases, the usage of multiple datasets and different objective functions can improve model identifiability. Finally, the developed software was used to perform parameter optimisation against two sets of action potentials from a sinoatrial node experiment, in the absence and presence of E9031, a specific ion channel blocker.
| Identifer | oai:union.ndltd.org:ADTP/272474 |
| Date | January 2009 |
| Creators | Hui, Ben Bunny Chun Bun, Graduate School of Biomedical Engineering, Faculty of Engineering, UNSW |
| Publisher | Awarded By:University of New South Wales. Graduate School of Biomedical Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright, http://unsworks.unsw.edu.au/copyright |
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