使用SMoRe ParS将基于代理的模型与高维参数空间连接到多维数据:一种替代模型方法
Daniel R Bergman1, Kerri-Ann Norton2, Harsh Vardhan Jain3
1Department of Mathematics, University of Michigan, 530 Church Street, Ann Arbor, MI, 48109, USA.
Bulletin of mathematical biology
|December 30, 2023
概括
我们开发了一种新的计算方法,SMoRe ParS,使用复杂的实验数据高效校准基于代理的模型 (ABM). 这种方法提高了ABM模拟用于生物和生物医学研究的准确性.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物医学信息学 生物医学信息学
背景情况:
- 基于代理的模型 (ABM) 对于理解复杂的生物系统至关重要.
- 使用高维参数空间和多尺度数据对ABM进行校准仍然是一个重大的计算挑战.
- 现有的方法很难有效地将ABM参数空间与多维实验数据连接起来.
研究的目的:
- 扩展和验证一种新的方法,即重建参数表面的替代模型 (SMoRe ParS),用于计算高效的ABM校准.
- 开发一个框架,将高维的ABM参数空间与多维数据连接起来.
- 用单维和多维实验数据证明SMoRe ParS在校准ABM中的有效性.
主要方法:
- 修改了SMoRe ParS,最初使用单维数据 (体外癌细胞生长分析) 来限制高维ABM参数空间.
- 将方法扩展到限制参数空间,使用在体外癌细胞抑制试验中的多维数据与氧沙.
- 通过使用SMoRe ParS推断参数与常用的直接方法比较ABM模拟准确度来验证方法.
主要成果:
- 扩展的SMoRe ParS框架有效地将ABM参数空间校准到多维数据.
- 使用SMoRe ParS推断参数导致了与实验数据密切匹配的ABM模拟.
- 替代模型在ABM和参数校准的实验数据之间起到有效的中间作用.
结论:
- SMoRe ParS提供了一个强大的和可扩展的战略,利用多维数据来告知多尺度ABM.
- 该方法可以有效地探索ABM参数空间和相关的不确定性.
- 这种方法提高了生物和生物医学研究中基于代理物的建模的预测能力和可靠性.
相关概念视频
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