Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
Bioinformatics. 2010 Apr 15;26(8):1064-72. doi: 10.1093/bioinformatics/btq080. Epub 2010 Mar 1.
Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse engineer a cellular system, called gene network, from time series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time series datasets under varying conditions to find critical differences between these networks. However, these comparisons often lead to misleading results, because each network contains considerable noise due to the limited length of the time series.
We propose an integrated approach for inferring multiple gene networks from time series expression data under varying conditions. To the best of our knowledge, our approach is the first reverse-engineering method that is intended for transcriptomic network comparison between varying conditions. Furthermore, we propose a state-of-the-art parameter estimation method, relevance-weighted recursive elastic net, for providing higher precision and recall than existing reverse-engineering methods. We analyze experimental data of MCF-7 human breast cancer cells stimulated by epidermal growth factor or heregulin with several doses and provide novel biological hypotheses through network comparison.
The software NETCOMP is available at http://bonsai.ims.u-tokyo.ac.jp/ approximately shima/NETCOMP/.
阐明细胞对各种生物条件或外部刺激的反应差异是系统生物学的一个重要挑战。已经开发了许多方法来从时间序列微阵列数据中反向工程细胞系统，称为基因网络，以便在感兴趣的条件下理解转录组反应。还基于从多个时间序列数据集在不同条件下独立推断的基因网络进行了比较拓扑分析，以找到这些网络之间的关键差异。然而，这些比较经常导致误导性的结果，因为由于时间序列的长度有限，每个网络都包含相当大的噪声。
我们提出了一种从不同条件下的时间序列表达数据中推断多个基因网络的综合方法。据我们所知，我们的方法是第一个旨在在不同条件下进行转录组网络比较的反向工程方法。此外，我们提出了一种最先进的参数估计方法，相关加权递归弹性网络，以提供比现有反向工程方法更高的精度和召回率。我们分析了表皮生长因子或人表皮生长因子受体配体刺激 MCF-7 人乳腺癌细胞的实验数据，并提供了通过网络比较得出的新的生物学假设。
软件 NETCOMP 可在 http://bonsai.ims.u-tokyo.ac.jp/ 上获得，大约是 shima/NETCOMP/。
