Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge
Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge
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DOI：
10.1186/1752-0509-1-11
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发表时间：
2007-02-02
影响因子：
--
通讯作者：
Fleck, Christian
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文献类型：
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作者：
Geier, Florian;Timmer, Jens;Fleck, Christian
背景：细胞过程由基因调控网络控制。目前有几种计算方法用于从数据中学习基因调控网络的结构。本研究的重点是时间序列基因表达和基因敲除数据，以确定潜在的网络结构。我们比较了不同的网络重建方法的性能，使用合成数据生成的合奏的参考网络。数据要求，以及最佳实验的基因调控网络的重建进行了研究。此外，先验知识对网络重建的影响，以及未观察到的细胞processes.Results的效果：我们确定线性高斯动态贝叶斯网络和变量选择的基础上F-统计作为合适的方法重建基因调控网络的时间序列数据。常用的离散动态贝叶斯网络表现较差，这一结果可以归因于表达数据的离散化不可避免的信息丢失。结果表明，短时间序列下产生的转录因子敲除是最佳的实验，以揭示基因调控网络的结构。相对于观测噪声的水平，我们给出了所需的基因表达数据量的估计，以准确地重建基因调控网络。在贝叶斯学习框架内使用先验知识的好处被发现限于小基因表达数据大小的条件。未观察到的过程，如蛋白质-蛋白质相互作用，诱导基因表达水平之间的依赖性，类似于直接转录调控。我们发现，这些依赖关系不能区分从转录因子介导的基因调控的基础上的基因表达dataolong.Conclusion：目前可用的数据量和数据质量的基因表达数据的基因网络的重建是一个挑战。在这项研究中，我们确定了一个最佳的实验类型，对基因表达数据的质量和大小的要求，以及适当的重建方法，以逆向工程基因调控网络的时间序列数据。
Background: Cellular processes are controlled by gene-regulatory networks. Several computational methods are currently used to learn the structure of gene-regulatory networks from data. This study focusses on time series gene expression and gene knock-out data in order to identify the underlying network structure. We compare the performance of different network reconstruction methods using synthetic data generated from an ensemble of reference networks. Data requirements as well as optimal experiments for the reconstruction of gene-regulatory networks are investigated. Additionally, the impact of prior knowledge on network reconstruction as well as the effect of unobserved cellular processes is studied.Results: We identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene- regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. Relative to the level of observational noise, we give estimates for the required amount of gene expression data in order to accurately reconstruct gene- regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. We show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone.Conclusion: Currently available data size and data quality make the reconstruction of gene networks from gene expression data a challenge. In this study, we identify an optimal type of experiment, requirements on the gene expression data quality and size as well as appropriate reconstruction methods in order to reverse engineer gene regulatory networks from time series data.