CoMPLEX, University College London, London, UK.
J R Soc Interface. 2012 Sep 7;9(74):2156-66. doi: 10.1098/rsif.2011.0891. Epub 2012 Apr 4.
One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.
生物系统数学和计算模型发展的主要挑战之一是精确估计参数值。了解参数值不确定性对模型行为的影响对于成功使用这些模型至关重要。全局敏感性分析（SA）可用于量化多个参数不确定性导致模型预测的变化，并阐明驱动系统行为的生物学机制。我们提出了一种新的系统生物学全局 SA 方法，该方法计算效率高，可用于确定关键参数及其相互作用，从而驱动复杂生物模型的动态行为。该方法结合了功能主成分分析和已建立的全局 SA 技术。该方法应用于胰岛素信号通路模型，该模型的缺陷是 2 型糖尿病的主要原因，并且确定了该系统的一些关键特征。
