Analysis of heterogeneous cell populations: A density-based modeling and identification framework
Analysis of heterogeneous cell populations: A density-based modeling and identification framework
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DOI：
10.1016/j.jprocont.2011.06.020
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发表时间：
2011-12-01
影响因子：
4.2
通讯作者：
Allgoewer, Frank
中科院分区：
文献类型：
--
作者：
Hasenauer, Jan;Waldherr, Steffen;Allgoewer, Frank
在许多生物学过程中，克隆细胞群体的异质性是一个重要的问题。其中一个最引人注目的例子是，在一个共同的、相同的死亡信号之后，一些细胞死亡，而另一些细胞存活。这种异质性的原因是内在的和外在的noise.In本文中，我们提出了一个机制的多尺度建模框架的细胞群体，其中每个细胞的动态参数依赖的随机微分方程（RSDs）捕获。单个细胞之间的异质性是由参数值的差异来解释的，建模外部影响。基于非本征噪声的统计特性和单个细胞的非线性模型，推导出了一个偏微分方程（PDE）模型。这个偏微分方程描述了人口密度的演变。为了从实验总体数据中确定外部噪声的统计量，推导了噪声破坏数据的基于密度的统计数据模型。采用这种数据模型，我们表明，可以使用凸优化计算的外部的统计。这种评估参数的有效方法允许通过bootstrapping.To评估所提出的方法，一个模型的caspase激活级联到目前为止不可行的不确定性分析。结果表明，对于已知的噪声特性，该模型中的未知参数密度可以通过所提出的方法得到很好的估计。2011爱思唯尔有限公司保留所有权利。
In many biological processes heterogeneity within clonal cell populations is an important issue. One of the most striking examples is a population of cancer cells in which after a common, identical death signal some cells die whereas others survive. The reason for this heterogeneity is intrinsic and extrinsic noise.In this paper we present a mechanistic multi-scale modeling framework for cell populations, in which the dynamics of every individual cell is captured by a parameter dependent stochastic differential equation (SDE). Heterogeneity among individual cells is accounted for by differences in parameter values, modeling extrinsic influences. Based on the statistical properties of the extrinsic noise and the SDE model for the individual cell, a partial differential equation (PDE) model is derived. This PDE describes the evolution of the population density. To determine the statistics of the extrinsic noise from experimental population data, a density-based statistical data model of the noise-corrupted data is derived. Employing this data model we show that the statistics of the extrinsic can be computed using a convex optimization. This efficient way of assessing the parameters allows for a so far infeasible uncertainty analysis via bootstrapping.To evaluate the proposed method, a model for the caspase activation cascade is considered. It is shown that for known noise properties the unknown parameter densities in this model are well estimated by the proposed method. 2011 Elsevier Ltd. All rights reserved.