层次贝叶斯模型的变化和不确定性在合成动作潜力痕迹的层次贝叶斯模型
Ross H Johnstone1, Rémi Bardenet2, David J Gavaghan1
1Computational Biology, Dept. of Computer Science, University of Oxford, Oxford, UK.
Computing in cardiology
|August 8, 2023
概括
这项研究量化了心肌细胞动力电位 (AP) 模型中的不确定性,成功推断了离子电流密度分布. 这提高了对药物效应的预测,尽管有一些参数无法识别.
科学领域:
- 计算生物学是一种计算生物学.
- 心血管研究的心血管研究.
- 生物物理学的生物物理.
背景情况:
- 心肌细胞动作潜力 (AP) 模型面临来自测量,参数和可变性的不确定性.
- 有单个参数值的现有模型限制了对药物效应的预测准确性.
研究的目的:
- 使用实验数据重新参数化AP模型并量化离子电流密度的不确定性.
- 为了将这种不确定性传播到模型预测中,例如药品阻断离子通道.
主要方法:
- 在不同的参数中使用合成数据进行的in silico研究.
- 阶层马尔科夫链蒙特卡洛 (MCMC) 方法用于重新推断参数分布.
- 在药物效应预测中不确定性传播的分析.
主要成果:
- 从AP痕迹中成功推断出大多数离子电流密度的分布.
- 采样参数值的高层分布的近似分布.
- 在特定的离子电流密度中发现了一些不可识别性.
结论:
- 层次的MCMC有效量化心肌细胞AP模型中的不确定性.
- 通过结合参数不确定性,提高了对药物效应的预测.
- 对某些离子电流的参数识别能力存在已知的局限性.
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