Computer and Information Sciences and Engineering, University of Florida, Gainesville, FL 32611, USA.
BMC Bioinformatics. 2012 Sep 28;13:250. doi: 10.1186/1471-2105-13-250.
Transcription factors regulate numerous cellular processes by controlling the rate of production of each gene. The regulatory relations are modeled using transcriptional regulatory networks. Recent studies have shown that such networks have an underlying hierarchical organization. We consider the problem of discovering the underlying hierarchy in transcriptional regulatory networks.
We first transform this problem to a mixed integer programming problem. We then use existing tools to solve the resulting problem. For larger networks this strategy does not work due to rapid increase in running time and space usage. We use divide and conquer strategy for such networks. We use our method to analyze the transcriptional regulatory networks of E. coli, H. sapiens and S. cerevisiae.
Our experiments demonstrate that: (i) Our method gives statistically better results than three existing state of the art methods; (ii) Our method is robust against errors in the data and (iii) Our method's performance is not affected by the different topologies in the data.
转录因子通过控制每个基因的产生速率来调节许多细胞过程。这些调控关系是通过转录调控网络来建模的。最近的研究表明，这些网络具有潜在的层次结构。我们考虑发现转录调控网络中潜在层次结构的问题。
我们首先将这个问题转化为一个混合整数规划问题。然后，我们使用现有的工具来解决这个问题。对于更大的网络，由于运行时间和空间使用的快速增加，这种策略是行不通的。我们对这种网络使用分治策略。我们使用我们的方法来分析大肠杆菌、人类和酿酒酵母的转录调控网络。
我们的实验表明：（i）我们的方法比现有的三种最先进的方法给出了统计上更好的结果；（ii）我们的方法对数据中的错误具有鲁棒性；（iii）我们的方法的性能不受数据中不同拓扑结构的影响。
