首页> 外文会议>2010 Sixth IEEE International Conference on e-Science >A Local Sensitivity Analysis Method for Developing Biological Models with Identifiable Parameters: Application to L-type Calcium Channel Modelling
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A Local Sensitivity Analysis Method for Developing Biological Models with Identifiable Parameters: Application to L-type Calcium Channel Modelling

机译:用于开发具有可识别参数的生物模型的局部灵敏度分析方法:在L型钙通道模型中的应用

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Computational cardiac models provide important insights into the underlying mechanisms of heart function. Parameter estimation in these models is an ongoing challenge with many existing models being overparameterised. Sensitivity analysis presents a key tool for exploring the parameter identifiability. While existing methods provide insight into the significance of the parameters, they are unable to identify redundant parameters in an efficient manner. We present a new singular value decomposition based algorithm for determining parameter identifiability in cardiac models. Using this local sensitivity approach, we investigate the Mahajan 2008 rabbit ventricular myocyte L-type calcium current model. We identify non-significant and redundant parameters and improve the Ical model by reducing it to a minimum one that is validated to have only identifiable parameters. The newly proposed approach provides a new method for model validation and evaluation of the predictive power of cardiac models.
机译:计算性心脏模型为深入了解心脏功能的潜在机制提供了重要见识。这些模型中的参数估计是一个持续的挑战,因为许多现有模型的参数都过高。灵敏度分析提供了一种探索参数可识别性的关键工具。尽管现有方法可以深入了解参数的重要性,但是它们无法以有效的方式识别冗余参数。我们提出了一种新的基于奇异值分解的算法,用于确定心脏模型中的参数可识别性。使用这种局部敏感性方法,我们调查了Mahajan 2008兔心室肌细胞L型钙电流模型。我们确定不重要的参数和冗余的参数,并通过将Ical模型减少到经验证仅具有可识别参数的最小值来改进Ical模型。新提出的方法为模型验证和评估心脏模型的预测能力提供了一种新方法。

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