Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, United States; Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
Department of Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Department of Mathematics, North Carolina State University, Raleigh, NC, United States.
J Theor Biol. 2021 Oct 7;526:110759. doi: 10.1016/j.jtbi.2021.110759. Epub 2021 May 11.
In this study, we develop a methodology for model reduction and selection informed by global sensitivity analysis (GSA) methods. We apply these techniques to a control model that takes systolic blood pressure and thoracic tissue pressure data as inputs and predicts heart rate in response to the Valsalva maneuver (VM). The study compares four GSA methods based on Sobol' indices (SIs) quantifying the parameter influence on the difference between the model output and the heart rate data. The GSA methods include standard scalar SIs determining the average parameter influence over the time interval studied and three time-varying methods analyzing how parameter influence changes over time. The time-varying methods include a new technique, termed limited-memory SIs, predicting parameter influence using a moving window approach. Using the limited-memory SIs, we perform model reduction and selection to analyze the necessity of modeling both the aortic and carotid baroreceptor regions in response to the VM. We compare the original model to systematically reduced models including (i) the aortic and carotid regions, (ii) the aortic region only, and (iii) the carotid region only. Model selection is done quantitatively using the Akaike and Bayesian Information Criteria and qualitatively by comparing the neurological predictions. Results show that it is necessary to incorporate both the aortic and carotid regions to model the VM.
在本研究中，我们开发了一种基于全局敏感性分析（GSA）方法的模型简化和选择方法。我们将这些技术应用于一个控制模型，该模型以收缩压和胸腔组织压力数据作为输入，并预测心率对瓦尔萨尔瓦动作（VM）的反应。该研究比较了基于 Sobol' 指数（SI）的四种 GSA 方法，这些指数量化了参数对模型输出与心率数据之间差异的影响。GSA 方法包括标准标量 SI，用于确定在研究的时间间隔内参数对平均影响，以及三种时变方法，用于分析参数影响随时间的变化情况。时变方法包括一种新技术，称为有限记忆 SI，该技术使用移动窗口方法预测参数影响。使用有限记忆 SI，我们进行模型简化和选择，以分析在 VM 响应中建模主动脉和颈动脉压力感受器区域的必要性。我们将原始模型与系统简化模型进行比较，包括（i）主动脉和颈动脉区域，（ii）仅主动脉区域，和（iii）仅颈动脉区域。使用赤池信息量准则和贝叶斯信息量准则进行定量模型选择，并通过比较神经预测进行定性模型选择。结果表明，有必要同时纳入主动脉和颈动脉区域来模拟 VM。
