Identification of models of heterogeneous cell populations from population snapshot data.
Identification of models of heterogeneous cell populations from population snapshot data.
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DOI：
10.1186/1471-2105-12-125
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发表时间：
2011-04-28
影响因子：
3
通讯作者：
Allgöwer F
中科院分区：
文献类型：
--
作者：
Hasenauer J;Waldherr S;Doszczak M;Radde N;Scheurich P;Allgöwer F
在系统生物学领域进行的大多数建模旨在实现对“典型细胞”内细胞内通路的定量描述。然而，在许多重要的生物学情况下，即使是克隆细胞群体也可能表现出异质反应。这些情况需要研究细胞间的可变性，并开发异质细胞群的模型。在本文中，我们考虑细胞群，其中每一个细胞的动力学是由一个参数相关的微分方程捕获。单元格之间的差异由受概率密度影响的参数差异来建模。提出了一种新的贝叶斯方法，从群体快照数据（如流式细胞分析）中推断出这种概率密度，而流式细胞分析不提供单细胞时间序列数据。该方法可以处理稀疏和有噪声的测量数据。此外，从应用的角度来看，它具有吸引力，因为与其他方法相比，所得参数分布的不确定性可以直接评估。利用肿瘤坏死因子信号网络模型的人工实验数据对所提出的方法进行了评估。结果表明，该方法计算效率高，即使对稀疏数据集也能产生良好的估计结果。
Most of the modeling performed in the area of systems biology aims at achieving a quantitative description of the intracellular pathways within a "typical cell". However, in many biologically important situations even clonal cell populations can show a heterogeneous response. These situations require study of cell-to-cell variability and the development of models for heterogeneous cell populations. In this paper we consider cell populations in which the dynamics of every single cell is captured by a parameter dependent differential equation. Differences among cells are modeled by differences in parameters which are subject to a probability density. A novel Bayesian approach is presented to infer this probability density from population snapshot data, such as flow cytometric analysis, which do not provide single cell time series data. The presented approach can deal with sparse and noisy measurement data. Furthermore, it is appealing from an application point of view as in contrast to other methods the uncertainty of the resulting parameter distribution can directly be assessed. The proposed method is evaluated using artificial experimental data from a model of the tumor necrosis factor signaling network. We demonstrate that the methods are computationally efficient and yield good estimation result even for sparse data sets.
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影响因子：
1.5
作者：
Gander, W;Gautschi, W
通讯作者：
Gautschi, W
影响因子：
2
作者：
Stamatakis, Michail;Zygourakis, Kyriacos
通讯作者：
Zygourakis, Kyriacos
影响因子：
6.8
作者：
Chaves, Madalena;Eissing, Thomas;Allgoewer, Frank
通讯作者：
Allgoewer, Frank
影响因子：
3.5
作者：
Banks, H. T.;Sutton, Karyn L.;Thompson, W. Clayton;Bocharov, Gennady;Roose, Dirk;Schenkel, Tim;Meyerhans, Andreas
通讯作者：
Meyerhans, Andreas
DOI：
10.1007/bf01062273
发表时间：
1990-08-01
期刊：
JOURNAL OF PHARMACOKINETICS AND BIOPHARMACEUTICS
影响因子：
--
作者：
ALBANNA, MK;KELMAN, AW;WHITING, B
通讯作者：
WHITING, B