在一个自我生成的梯度集体细胞运动模型中的近似贝叶斯推理
Jon Devlin1, Agnieszka Borowska2, Dirk Husmeier2
1Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.
概括
本研究将近似贝叶斯计算 (ABC) 方法与细胞运动模型中的参数推理进行比较. 它确定了高性能的ABC算法,用于分析具有随机过程的复杂生物系统.
科学领域:
- 计算生物学 计算生物学
- 数学建模的数学建模
- 生物物理学的生物物理.
背景情况:
- 对复杂的生物模型进行参数推断,比如细胞在化疗梯度中的运动,具有挑战性.
- 细胞运动模型中的随机过程往往使传统的基于概率的推理在计算上难以处理.
- 大致贝叶斯计算 (ABC) 为具有计算昂贵或难以处理的可能性的模型提供了可行的替代方案.
研究的目的:
- 评估和比较各种近似贝叶斯计算 (ABC) 算法的性能.
- 在新型混合离散连续细胞运动模型中确定最适合参数推断的ABC方法.
- 评估ABC方法的准确性和偏差与基准随机微分方程 (SDE) 模型相比.
主要方法:
- 采用混合离散连续模型,用于细胞群体运动,以应对化疗.
- 由于模型的复杂性,采用近似贝叶斯计算 (ABC) 技术进行参数推理.
- 使用漂移-扩散随机微分方程 (SDE) 作为算法评估的基准.
- 与SDE模型的精确后部相比,评估了ABC后部的准确性和偏差.
主要成果:
- 在基准SDE模型中识别和排名了参数推理的高性能ABC算法.
- 证明了选择的高性能ABC算法的适用于复杂的细胞运动模型的适用性.
- 量化了ABC衍生后部与SDE真正后部分布之间的偏差.
结论:
- 该研究在生物建模的背景下对ABC算法效率进行了比较分析.
- 选择的ABC方法在复杂的细胞运动模型中对参数推理是有效的.
- 这项工作有助于选择合适的计算方法来分析复杂的生物系统.
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