A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium.
A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium.
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DOI：
10.1371/journal.pcbi.1005688
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发表时间：
2017-07
影响因子：
4.3
通讯作者：
Maini PK
中科院分区：
文献类型：
--
作者：
Maclaren OJ;Parker A;Pin C;Carding SR;Watson AJM;Fletcher AG;Byrne HM;Maini PK
我们的工作解决了两个关键挑战，一个是生物学挑战，一个是方法学挑战。首先，我们的目标是了解在健康，受损（阿糖胞苷处理）和恢复条件下，肠上皮细胞的增殖和细胞迁移率是如何相关的，以及这些关系如何用于识别修复和再生机制。我们分析了新的数据，更详细地介绍了在配套文件中，其中BrdU/IdU细胞标记实验在这些各自的条件下进行。其次，在考虑如何更严格地处理这些数据并使用数学模型解释它们时，我们使用概率分层方法。这为系统地建模和理解不确定性提供了最佳实践方法，否则这些不确定性会破坏可靠结论的产生-实验测量和治疗中的不确定性，难以比较的基本机制数学模型以及未知或未观察到的参数。空间离散和连续的机械模型被认为是通过分层的条件概率假设和相关。我们对样本内和样本外数据集进行模型检查，并使用它们来展示如何测试可能的模型改进并评估我们结论的鲁棒性。我们的结论是，对于目前的一组实验，一个主要的增殖驱动的模型足以预测标记的细胞动力学在大多数时间尺度。肠上皮是研究多细胞群体动态和调控的重要模型系统。它的特点是快速的自我更新和修复;这些过程的失调被认为部分解释了为什么许多肿瘤在肠道和类似的上皮组织中形成。这些特点导致了大量的工作，估计细胞动力学参数在肠道。然而，在收集的原始数据、对这些实验数据的解释以及描述基本过程的机械模型之间仍然存在很大的差距。分层统计建模提供了一种自然的方法来弥合这些差距，但迄今为止，在肠组织自我更新的研究中未得到充分利用。正如我们所示，这种方法使得基本上使用的“测量”，“过程”和“参数”模型之间的区别，给出了一个明确的框架相结合的实验数据和机械建模的存在多个来源的不确定性。我们应用这种方法来分析健康，受损和恢复肠道组织的实验，发现观察到的数据可以用一个模型来解释，在这个模型中，细胞运动主要由增殖驱动。
Our work addresses two key challenges, one biological and one methodological. First, we aim to understand how proliferation and cell migration rates in the intestinal epithelium are related under healthy, damaged (Ara-C treated) and recovering conditions, and how these relations can be used to identify mechanisms of repair and regeneration. We analyse new data, presented in more detail in a companion paper, in which BrdU/IdU cell-labelling experiments were performed under these respective conditions. Second, in considering how to more rigorously process these data and interpret them using mathematical models, we use a probabilistic, hierarchical approach. This provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine the generation of reliable conclusions—uncertainties in experimental measurement and treatment, difficult-to-compare mathematical models of underlying mechanisms, and unknown or unobserved parameters. Both spatially discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions. We perform model checks on both in-sample and out-of-sample datasets and use them to show how to test possible model improvements and assess the robustness of our conclusions. We conclude, for the present set of experiments, that a primarily proliferation-driven model suffices to predict labelled cell dynamics over most time-scales. The intestinal epithelium is an important model system for studying the dynamics and regulation of multicellular populations. It is characterised by rapid rates of self-renewal and repair; dysregulation of these processes is thought to explain, in part, why many tumours form in the intestinal and similar epithelial tissues. These features have led to a large amount of work on estimating cell kinetic parameters in the intestine. There remain, however, large gaps between the raw data collected, the interpretation of these experimental data, and mechanistic models that describe the underlying processes. Hierarchical statistical modelling provides a natural method with which to bridge these gaps, but has, to date, been underutilised in the study of intestinal tissue self-renewal. As we illustrate, this approach makes essential use of the distinction between ‘measurement’, ‘process’ and ‘parameter’ models, giving an explicit framework for combining experimental data and mechanistic modelling in the presence of multiple sources of uncertainty. We apply this approach to analyse experiments on healthy, damaged and recovering intestinal tissue, finding that observed data can be explained by a model in which cell movement is driven primarily by proliferation.
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DOI：
10.1039/c3ib40163d
发表时间：
2014-03
期刊：
Integrative biology : quantitative biosciences from nano to macro
影响因子：
--
作者：
Carulli AJ;Samuelson LC;Schnell S
通讯作者：
Schnell S
DOI：
10.1111/j.2044-8317.2011.02037.x
发表时间：
2013-02
期刊：
The British journal of mathematical and statistical psychology
影响因子：
--
作者：
Gelman A;Shalizi CR
通讯作者：
Shalizi CR
DOI：
10.1093/imammb/dqp015
发表时间：
2010-03-01
影响因子：
1.1
作者：
Fozard, J. A.;Byrne, H. M.;King, J. R.
通讯作者：
King, J. R.
影响因子：
2.4
作者：
Hywood, Jack D.;Hackett-Jones, Emily J.;Landman, Kerry A.
通讯作者：
Landman, Kerry A.
DOI：
10.2307/2982063
发表时间：
1980-01-01
影响因子：
2
作者：
BOX, GEP
通讯作者：
BOX, GEP