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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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梯度匹配加速了对生物化学网络的混合效应推断.

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概括
此摘要是机器生成的。

我们介绍了渐变匹配全球两阶段 (GMGTS),这是分析单细胞数据变异性的更快方法. 这种方法可以降低系统生物学中非线性混合效应模型的计算成本.

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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:

  • 单细胞时间序列数据显示了显著的人口变化.
  • 模拟细胞内过程需要反映这种变化的参数分布,而不仅仅是人口平均值.
  • 全球双阶段 (GTS) 是非线性混合效应模型 (NLME) 的常用方法,但在计算上是密集的.

研究的目的:

  • 为NLME模型中参数估计开发GTS方法的高效替代方案.
  • 为了减少与分析单细胞数据变异性相关的计算负担.
  • 提高复杂的NLME模型在系统生物学研究中的适用性.

主要方法:

  • 提出梯度匹配GTS (GMGTS),一种没有集成的参数估计技术.
  • 将梯度匹配纳入GTS框架,用于不确定性传播和代估计.
  • 利用参数线性系统,例如具有质量动力动态的生物化学网络.

主要成果:

  • 与标准GTS方法相比,GMGTS显著降低了计算需求.
  • 该方法促进了复杂的NLME模型用于分析细胞变异性的应用.
  • 对于部分观察到的系统,GMGTS使不确定性传播和代估计成为可能.

结论:

  • 在NLME模型中,GMGTS为参数估计提供了一个计算效率高的替代方案.
  • 这种方法对于分析具有固有的可变性的单细胞时间序列数据尤为有益.
  • 开发的方法扩大了NLME在系统生物学中的建模能力.