A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data
A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data
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DOI：
10.1016/j.jtbi.2020.110541
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发表时间：
2021-02-21
影响因子：
2
通讯作者：
Tavener, Simon J.
中科院分区：
文献类型：
--
作者：
Lambert, Ben;Gavaghan, David J.;Tavener, Simon J.
变异是所有生命系统的特征。流式细胞术等实验室技术可以探测单个细胞，经过数十年的实验，很明显，即使是基因相同的细胞群的成员也可能表现出差异。要了解变异是否具有生物学意义，必须辨别其来源。生物系统的数学模型是可用于研究细胞间变异原因的工具。通过对这些模型的数学分析和模拟，可以提出并研究生物学假设，然后参数推断可以确定哪些假设与实验数据兼容。来自实验室实验的数据通常由代表不同时间点细胞特性分布的“快照”组成，而不是单个细胞的轨迹。使用分层贝叶斯方法无法直接拟合这些数据，该方法需要先验选择细胞群簇的数量。它们也不适合标准的非线性混合效应方法，因为每个细胞的单个观察通常太少而无法估计参数变异性。在这里，我们引入了一种名为“轮廓蒙特卡罗”（CMC）的计算采样方法，用于根据快照分布估计数学模型参数，该方法易于实现，并且不需要将单元格分配给预定义的类别。 CMC 算法适合快照概率分布而不是原始数据，这意味着它的计算负担不会像现有方法那样随着观察到的细胞数量而增加。我们的方法适用于欠定系统，在这种系统中，不同类型的观察结果比待确定的参数要少，并且观察到的变化主要是由于细胞过程的变化而不是实验测量误差。由于实验室技术分辨率的不断提高，许多系统可能都是这种情况。在本文中，我们应用我们的方法来量化三个感兴趣的生物系统的细胞变异，并提供 Julia 代码，使其他人能够使用该方法。 (C) 2020 Elsevier Ltd. 保留所有权利。
Variation is characteristic of all living systems. Laboratory techniques such as flow cytometry can probe individual cells, and, after decades of experimentation, it is clear that even members of genetically identical cell populations can exhibit differences. To understand whether variation is biologically meaningful, it is essential to discern its source. Mathematical models of biological systems are tools that can be used to investigate causes of cell-to-cell variation. From mathematical analysis and simulation of these models, biological hypotheses can be posed and investigated, then parameter inference can determine which of these is compatible with experimental data. Data from laboratory experiments often consist of "snapshots" representing distributions of cellular properties at different points in time, rather than individual cell trajectories. These data are not straightforward to fit using hierarchical Bayesian methods, which require the number of cell population clusters to be chosen a priori. Nor are they amenable to standard nonlinear mixed effect methods, since a single observation per cell is typically too few to estimate parameter variability. Here, we introduce a computational sampling method named "Contour Monte Carlo" (CMC) for estimating mathematical model parameters from snapshot distributions, which is straightforward to implement and does not require that cells be assigned to predefined categories. The CMC algorithm fits to snapshot probability distributions rather than raw data, which means its computational burden does not, like existing approaches, increase with the number of cells observed. Our method is appropriate for underdetermined systems, where there are fewer distinct types of observations than parameters to be determined, and where observed variation is mostly due to variability in cellular processes rather than experimental measurement error. This may be the case for many systems due to continued improvements in resolution of laboratory techniques. In this paper, we apply our method to quantify cellular variation for three biological systems of interest and provide Julia code enabling others to use this method. (C) 2020 Elsevier Ltd. All rights reserved.