A probabilistic graphical model for system-wide analysis of gene regulatory networks
A probabilistic graphical model for system-wide analysis of gene regulatory networks
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DOI：
10.1093/bioinformatics/btaa122
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发表时间：
2020-05-15
期刊：
影响因子：
5.8
通讯作者：
Eslami, Ali
中科院分区：
文献类型：
--
作者：
Kotiang, Stephen;Eslami, Ali
动机：从DNA微阵列测量中推断基因调控网络（GRNs）形成了基于系统生物学的表型分析的核心要素。在最近的过去，许多计算方法已被正式化，使可靠的和可测试的预测在今天的生物学推导。然而，很少有人关注现有的最先进的GRNs与测量的基因表达谱的对应程度。在这里，我们提出了一个计算框架，结合概率图形建模，标准统计估计，高集成度，通过生物数据来探索生物系统的全局行为以及实验验证的GRN和相应的GRN之间的全局一致性。大微阵列概要数据。该模型被表示为概率二分图，它可以处理高度复杂的网络系统，并容纳不同的生物实体，例如信使RNA，蛋白质，代谢物和各种刺激参与调节网络的部分测量。该方法在来自M3D数据库的微阵列表达数据上进行了测试，该数据库对应于研究最好的模式生物之一大肠杆菌的子网络。结果表明，在各种实验条件下，观察到的状态和推断的系统的行为之间的相关性令人惊讶的高。
Motivation: The inference of gene regulatory networks (GRNs) from DNA microarray measurements forms a core element of systems biology-based phenotyping. In the recent past, numerous computational methodologies have been formalized to enable the deduction of reliable and testable predictions in today's biology. However, little focus has been aimed at quantifying how well existing state-of-the-art GRNs correspond to measured gene-expression profiles.Results: Here, we present a computational framework that combines the formulation of probabilistic graphical modeling, standard statistical estimation, and integration of high-throughput biological data to explore the global behavior of biological systems and the global consistency between experimentally verified GRNs and corresponding large microarray compendium data. The model is represented as a probabilistic bipartite graph, which can handle highly complex network systems and accommodates partial measurements of diverse biological entities, e.g. messengerRNAs, proteins, metabolites and various stimulators participating in regulatory networks. This method was tested on microarray expression data from the M3D database, corresponding to sub-networks on one of the best researched model organisms, Escherichia coli. Results show a surprisingly high correlation between the observed states and the inferred system's behavior under various experimental conditions.