Single-cell behavior and population heterogeneity: solving an inverse problem to compute the intrinsic physiological state functions.
Single-cell behavior and population heterogeneity: solving an inverse problem to compute the intrinsic physiological state functions.
复制标题
单细胞行为和群体异质性：解决反问题以计算内在的生理状态函数。
DOI：
10.1016/j.jbiotec.2011.08.018
复制
发表时间：
2012
影响因子：
4.1
通讯作者：
Zygourakis,Kyriacos
中科院分区：
文献类型：
--
作者：
Spetsieris,Konstantinos;Zygourakis,Kyriacos
同基因细胞群体的动态可以通过解释表型异质性的细胞群体平衡模型来描述。然而，为了利用这些模型的预测能力，我们必须知道单细胞反应和分裂的速率以及二元分区概率密度函数。这三个内在生理状态（IPS）函数可以通过求解逆问题来获得，所述逆问题需要关于总细胞群、分裂细胞亚群和新生细胞亚群的表型分布的知识。我们在这里提出了一个强大的计算程序，可以准确地估计IPS功能的异质细胞群体。详细的参数分析表明，逆解的精度是如何影响离散化参数，所使用的非参数估计的类型，表型分布的定性特征和未知的分区概率密度函数。还评估了有限采样和测量误差对恢复的IPS功能准确性的影响。最后，我们应用该方法估计了E.携带IPTG诱导的遗传切换网络的大肠杆菌群体。这项研究完成了一个集成的实验和计算框架的开发，可以成为一个强大的工具，用于量化单细胞行为使用测量异质细胞群体。
The dynamics of isogenic cell populations can be described by cell population balance models that account for phenotypic heterogeneity. To utilize the predictive power of these models, however, we must know the rates of single-cell reaction and division and the bivariate partition probability density function. These three intrinsic physiological state (IPS) functions can be obtained by solving an inverse problem that requires knowledge of the phenotypic distributions for the overall cell population, the dividing cell subpopulation and the newborn cell subpopulation. We present here a robust computational procedure that can accurately estimate the IPS functions for heterogeneous cell populations. A detailed parametric analysis shows how the accuracy of the inverse solution is affected by discretization parameters, the type of non-parametric estimators used, the qualitative characteristics of phenotypic distributions and the unknown partitioning probability density function. The effect of finite sampling and measurement errors on the accuracy of the recovered IPS functions is also assessed. Finally, we apply the procedure to estimate the IPS functions of an E. coli population carrying an IPTG-inducible genetic toggle network. This study completes the development of an integrated experimental and computational framework that can become a powerful tool for quantifying single-cell behavior using measurements from heterogeneous cell populations.