Department of Mathematics, North Carolina State University, Raleigh, NC, USA.
Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
Bull Math Biol. 2019 Jun;81(6):1805-1828. doi: 10.1007/s11538-019-00578-0. Epub 2019 Feb 28.
The complexity and size of state-of-the-art cell models have significantly increased in part due to the requirement that these models possess complex cellular functions which are thought-but not necessarily proven-to be important. Modern cell models often involve hundreds of parameters; the values of these parameters come, more often than not, from animal experiments whose relationship to the human physiology is weak with very little information on the errors in these measurements. The concomitant uncertainties in parameter values result in uncertainties in the model outputs or quantities of interest (QoIs). Global sensitivity analysis (GSA) aims at apportioning to individual parameters (or sets of parameters) their relative contribution to output uncertainty thereby introducing a measure of influence or importance of said parameters. New GSA approaches are required to deal with increased model size and complexity; a three-stage methodology consisting of screening (dimension reduction), surrogate modeling, and computing Sobol' indices, is presented. The methodology is used to analyze a physiologically validated numerical model of neurovascular coupling which possess 160 uncertain parameters. The sensitivity analysis investigates three quantities of interest, the average value of [Formula: see text] in the extracellular space, the average volumetric flow rate through the perfusing vessel, and the minimum value of the actin/myosin complex in the smooth muscle cell. GSA provides a measure of the influence of each parameter, for each of the three QoIs, giving insight into areas of possible physiological dysfunction and areas of further investigation.
由于需要这些模型具有复杂的细胞功能，而这些功能被认为（但不一定被证明）很重要，因此，最先进的细胞模型的复杂性和规模已经大大增加。现代细胞模型通常涉及数百个参数；这些参数的值通常来自动物实验，这些实验与人类生理学的关系很弱，关于这些测量中的误差的信息很少。参数值的伴随不确定性导致模型输出或感兴趣的数量（QoI）的不确定性。全局敏感性分析（GSA）旨在将个体参数（或参数集）的相对贡献分配给输出不确定性，从而引入了参数的影响或重要性的度量。需要新的 GSA 方法来处理增加的模型大小和复杂性；提出了一种由筛选（维度减少）、替代模型和计算 Sobol' 指数组成的三阶段方法。该方法用于分析具有 160 个不确定参数的神经血管耦合的生理验证数值模型。敏感性分析研究了三个感兴趣的数量，即细胞外空间中[Formula: see text]的平均值、通过灌注血管的平均体积流量以及平滑肌细胞中肌动球蛋白复合物的最小值。GSA 为每个 QoI 的每个参数提供了影响的度量，深入了解可能出现生理功能障碍的区域和进一步研究的区域。
