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首页> 外文期刊>Journal of Theoretical Biology >A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data
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A Monte Carlo method to estimate cell population heterogeneity from cell snapshot data

机译：从细胞快照数据估算细胞群异质性的蒙特卡罗方法

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摘要

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.
机译：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，经过几十年的实验，很明显，即使是基因相同的细胞群体成员也可能表现出差异。要了解变异是否具有生物学意义，必须弄清其来源。生物系统的数学模型是可以用来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，然后通过参数推理确定其中哪些与实验数据相符。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拟合这些数据并不容易，因为分层贝叶斯方法需要事先选择细胞群的数量。它们也不适用于标准的非线性混合效应方法，因为每个单元的单个观测值通常太少，无法估计参数的可变性。在这里，我们介绍了一种名为“轮廓蒙特卡罗”（CMC）的计算采样方法，用于从快照分布估计数学模型参数，该方法易于实现，并且不需要将单元分配到预定义的类别。CMC算法适用于快照概率分布，而不是原始数据，这意味着它的计算负担不会像现有方法一样，随着观察到的单元数的增加而增加。我们的方法适用于欠定系统，在欠定系统中，不同类型的观测比待确定的参数少，且观测到的变化主要是由于细胞过程中的变化，而不是实验测量误差。由于实验室技术分辨率的不断提高，许多系统可能就是这种情况。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，使其他人能够使用这种方法。（C） 2020爱思唯尔有限公司版权所有。

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