Bayesian Learning from Marginal Data in Bionetwork Models
Bayesian Learning from Marginal Data in Bionetwork Models
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DOI：
10.2202/1544-6115.1684
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发表时间：
2011-01-01
影响因子：
0.9
通讯作者：
West, Mike
中科院分区：
文献类型：
--
作者：
Bonassi, Fernando V.;You, Lingchong;West, Mike
在系统生物学动态分子网络的研究中，实验越来越多地利用流式细胞术等技术，在快照时间内生成少数网络节点的边缘分布数据。例如，一些基因或细胞表面蛋白标记的细胞内表达水平可以在一系列的过渡时间点进行检测，并在实验刺激的小细胞系统生长条件下假设稳定状态。这种关于少量细胞标记的边际数据通常会携带关于动态网络模型参数和结构的非常有限的信息，尽管实验通常旨在揭示与模型参数化和结构的某些方面内在相关的细胞表型变化。我们的工作解决了如何将这些数据与动态随机模型相结合的统计问题，以便适当地量化与假设模型相关的信息或缺乏信息。我们提出了一种贝叶斯计算策略，结合了一种新的方法来总结和数字表征生物表型，这些表型是根据细胞标记物的结果样本分布来表示的。我们在贝叶斯仿真方法和混合建模的基础上，定义了将网络动力学的机械数学模型与快照数据联系起来的方法，使用了一个将模拟数据和真实数据集成在一起的拨动开关示例作为上下文。
In studies of dynamic molecular networks in systems biology, experiments are increasingly exploiting technologies such as flow cytometry to generate data on marginal distributions of a few network nodes at snapshots in time. For example, levels of intracellular expression of a few genes, or cell surface protein markers, can be assayed at a series of interim time points and assumed steady-states under experimentally stimulated growth conditions in small cellular systems. Such marginal data on a small number of cellular markers will typically carry very limited information on the parameters and structure of dynamic network models, though experiments will typically be designed to expose variation in cellular phenotypes that are inherently related to some aspects of model parametrization and structure. Our work addresses statistical questions of how to integrate such data with dynamic stochastic models in order to properly quantify the information-or lack of information-it carries relative to models assumed. We present a Bayesian computational strategy coupled with a novel approach to summarizing and numerically characterizing biological phenotypes that are represented in terms of the resulting sample distributions of cellular markers. We build on Bayesian simulation methods and mixture modeling to define the approach to linking mechanistic mathematical models of network dynamics to snapshot data, using a toggle switch example integrating simulated and real data as context.