连接细胞周期建模的不同方法:从基于个体的模型中学习普通微分方程
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
本研究探讨在PhysiCell.中从复杂细胞模拟 (IBM) 中创建简化的数学模型 (代用ODEs). 这种方法可以降低计算成本,同时保持多尺度建模的准确性.
科学领域:
- 计算生物学 计算生物学
- 数学建模的数学建模
- 系统生物学 系统生物学
背景情况:
- 在PhysiCell中,基于个体的模型 (IBM) 提供了详细的细胞模拟.
- 导出简化的数学表示,如普通微分方程 (ODEs),可以减少计算负担.
- 混合离散连续多尺度系统需要高效的建模方法.
研究的目的:
- 为了研究从PhysiCell模拟的IBM产生代用ODEs的可行性.
- 展示机器学习的应用来构建这些替代模型.
- 评估衍生代用模型的准确性和计算效率.
主要方法:
- 使用PhysiCell平台模拟了各种细胞循环模型.
- 评估模拟特征,包括随机性,计算成本和准确性.
- 应用了稀疏识别工具SINDy-SA来识别替代ODE的数学结构.
主要成果:
- PhysiCell模拟的准确性和成本取决于时间步骤和初始人口等参数.
- 在SINDy-SA框架中,成功地确定了所有测试的细胞循环模型的ODE结构.
- 替代模型展示了降低计算成本的潜力,同时保持了准确性.
结论:
- 从PhysiCell IBM中导出代用ODE是可以实现的.
- SINDy-SA是构建这些代孕模型的有效工具.
- 这种方法有助于建模混合离散连续多尺度系统,并开发高效的替代模型.
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