Parameter Sensitivity Analysis of Stochastic Models Provides Insights into Cardiac Calcium Sparks
Parameter Sensitivity Analysis of Stochastic Models Provides Insights into Cardiac Calcium Sparks
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DOI：
10.1016/j.bpj.2012.12.055
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发表时间：
2013-03-05
影响因子：
3.4
通讯作者：
Sobie, Eric A.
中科院分区：
文献类型：
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作者：
Lee, Young-Seon;Liu, Ona Z.;Sobie, Eric A.
我们提出了一个参数的敏感性分析方法，是适当的随机模型，我们演示了如何分析产生实验可测试的预测的因素，影响当地的Ca2+释放在心脏细胞。该方法涉及随机改变所有参数，使用每组参数运行单个模拟，使用数百个模型变量运行模拟，然后使用回归方法将参数与模拟结果统计相关。我们测试了这种方法的随机模型，包含18个参数，心脏Ca2+火花。结果表明，多变量线性回归可以成功地将参数与连续模型输出（如Ca2+火花幅度和持续时间）联系起来，多变量逻辑回归可以深入了解参数如何影响Ca2+火花触发（一个概率过程，在单个模拟中是全或无）。基准研究表明，这种方法比标准方法的计算密集度低16倍。重要的是，通过测量敲除肌浆网蛋白三联蛋白的小鼠中的Ca2+火花来实验性地测试预测。这些小鼠在Ca2+释放单元结构中表现出多种变化，回归模型准确地预测了Ca2+火花振幅的变化（模型中减少30%，实验中减少29%），并提供了对每个变化对结果的贡献程度的直观和定量理解。因此，这种方法是一种有效的，高效的，和预测的方法，用于分析随机数学模型，以获得生物学的见解。
We present a parameter sensitivity analysis method that is appropriate for stochastic models, and we demonstrate how this analysis generates experimentally testable predictions about the factors that influence local Ca2+ release in heart cells. The method involves randomly varying all parameters, running a single simulation with each set of parameters, running simulations with hundreds of model variants, then statistically relating the parameters to the simulation results using regression methods. We tested this method on a stochastic model, containing 18 parameters, of the cardiac Ca2+ spark. Results show that multivariable linear regression can successfully relate parameters to continuous model outputs such as Ca2+ spark amplitude and duration, and multivariable logistic regression can provide insight into how parameters affect Ca2+ spark triggering (a probabilistic process that is all-or-none in a single simulation). Benchmark studies demonstrate that this method is less computationally intensive than standard methods by a factor of 16. Importantly, predictions were tested experimentally by measuring Ca2+ sparks in mice with knockout of the sarcoplasmic reticulum protein triadin. These mice exhibit multiple changes in Ca2+ release unit structures, and the regression model both accurately predicts changes in Ca2+ spark amplitude (30% decrease in model, 29% decrease in experiments) and provides an intuitive and quantitative understanding of how much each alteration contributes to the result. This approach is therefore an effective, efficient, and predictive method for analyzing stochastic mathematical models to gain biological insight.