Research group Bioinformatics and Systems Biology, Clinic for Internal Medicine I, University Medical Center Ulm, Ulm, Germany.
Bioinformatics. 2011 Jun 1;27(11):1529-36. doi: 10.1093/bioinformatics/btr166. Epub 2011 Apr 5.
Accurate, context-specific regulation of gene expression is essential for all organisms. Accordingly, it is very important to understand the complex relations within cellular gene regulatory networks. A tool to describe and analyze the behavior of such networks are Boolean models. The reconstruction of a Boolean network from biological data requires identification of dependencies within the network. This task becomes increasingly computationally demanding with large amounts of data created by recent high-throughput technologies. Thus, we developed a method that is especially suited for network structure reconstruction from large-scale data. In our approach, we took advantage of the fact that a specific transcription factor often will consistently either activate or inhibit a specific target gene, and this kind of regulatory behavior can be modeled using monotone functions.
To detect regulatory dependencies in a network, we examined how the expression of different genes correlates to successive network states. For this purpose, we used Pearson correlation as an elementary correlation measure. Given a Boolean network containing only monotone Boolean functions, we prove that the correlation of successive states can identify the dependencies in the network. This method not only finds dependencies in randomly created artificial networks to very high percentage, but also reconstructed large fractions of both a published Escherichia coli regulatory network from simulated data and a yeast cell cycle network from real microarray data.
准确、特定于上下文的基因表达调控对于所有生物都是至关重要的。因此，了解细胞基因调控网络内部的复杂关系非常重要。描述和分析此类网络行为的工具是布尔模型。从生物数据中重建布尔网络需要识别网络内的依赖关系。随着最近高通量技术产生的大量数据，这项任务的计算需求变得越来越大。因此，我们开发了一种特别适合从大规模数据中重建网络结构的方法。在我们的方法中，我们利用了这样一个事实，即特定的转录因子通常会一致地激活或抑制特定的靶基因，这种调控行为可以使用单调函数来建模。
为了在网络中检测调控依赖关系，我们检查了不同基因的表达如何与连续的网络状态相关。为此，我们使用 Pearson 相关作为基本的相关度量。对于仅包含单调布尔函数的布尔网络，我们证明了连续状态的相关性可以识别网络中的依赖关系。该方法不仅可以在随机创建的人工网络中以非常高的百分比找到依赖关系，而且还可以从模拟数据重建大肠杆菌调控网络的很大一部分，以及从真实微阵列数据重建酵母细胞周期网络的很大一部分。
