数学基础和工具链用于生物化学网络的层次优化
Nisha Ann Viswan1,2, Alexandre Tribut3,4, Manvel Gasparyan3
1National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India.
PLoS computational biology
|December 2, 2024
概括
我们开发了系统模拟的层次优化 (HOSS),以简化复杂的生物模型. 这种框架有效地优化路径块,提高模型准确性,并提供对参数退化的洞察力.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 生物信号系统本质上是复杂的,对机械模型构成重大挑战.
- 现有的建模方法与大参数空间,稀疏数据和多尺度/多物理现象作斗争.
研究的目的:
- 引入系统模拟的层次优化 (HOSS),这是解决复杂生物系统建模的新框架.
- 为了证明HOSS能够有效优化和管理大规模生物模型的能力.
主要方法:
- HOSS将广泛的系统模型分解为层次组织,嵌套的路径块.
- 每个独立的路径都被顺序优化,并为后续的级别设定了参数.
- 开发了对必要的嵌套层次结构的算法识别和可并行的变体,以增强模型生成.
主要成果:
- 在层次结构中证明了单个路径的高效优化.
- 可并行的HOSS变体产生了优异的模型和估计的溶液退化.
- 在抽象,基于事件和基于ODE的模拟类型中验证了有效性.
结论:
- HOSS为构建和优化复杂的生物系统模型提供了有效的框架.
- 层次方法简化了参数空间探索,并提高了模型的稳定性.
- HOSS适用于系统生物学中的各种模拟方法.
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