校准方法使参数符合复杂的生物模型中的参数
Pariksheet Nanda1, Denise E Kirschner1
1Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States.
Frontiers in applied mathematics and statistics
|January 15, 2024
概括
复杂的生物模型需要先进的校准. 本研究将校准协议 (CaliPro) 与近似贝叶斯计算 (ABC) 进行比较,为各种数据集选择适当的校准方法提供了更新的方法和决策树.
科学领域:
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
- 数学建模的数学建模
背景情况:
- 生物模型越来越复杂,整合了多尺度的方法 (ODEs,PDEs,基于代理的模型).
- 将这些模型与实验数据相匹配是具有挑战性的,因为参数空间和自由度很大.
- 目前的参数估计方法往往缺乏标准化和全面评估.
研究的目的:
- 审查,测试和推进复杂生物系统的模型校准实践.
- 为了比较校准协议 (CaliPro) 与近似贝叶斯计算 (ABC) 的性能.
- 为基于模型和数据集特征选择校准方法提供指导.
主要方法:
- 对校准方法的比较分析,包括CaliPro和ABC.
- 对模型不可知性校准实践的评估.
- 为CaliPro.Pro开发下一代更新的开发.
- 探索各种模型实现和数据集类型.
主要成果:
- 确定了CaliPro和ABC之间的优点,弱点,协同作用和差异.
- 证明了CaliPro在校准复杂生物模型中的性能.
- 介绍了CaliPro框架的更新功能.
结论:
- 标准化的校准方法对于可靠的生物模型适配至关重要.
- 卡利普罗为模型校准提供了一种强大而可适应的方法.
- 决策树有助于为特定的建模挑战选择最佳校准策略.
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