Emmert-Streib Frank, Dehmer Matthias
Center for Cancer Research and Cell Biology, Queen's University Belfast, UK.
BMC Syst Biol. 2009 Jul 20;3:76. doi: 10.1186/1752-0509-3-76.
Gene networks are a representation of molecular interactions among genes or products thereof and, hence, are forming causal networks. Despite intense studies during the last years most investigations focus so far on inferential methods to reconstruct gene networks from experimental data or on their structural properties, e.g., degree distributions. Their structural analysis to gain functional insights into organizational principles of, e.g., pathways remains so far under appreciated.
In the present paper we analyze cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions and not just associations or correlations between genes, and a list of known periodic genes. No further data are used. Partitioning the transcriptional regulatory network according to a graph theoretical property leads to a hierarchy in the network and, hence, in the information flow allowing to identify two groups of periodic genes. This reveals a novel conceptual interpretation of the working mechanism of the cell cycle and the genes regulated by this pathway.
Aside from the obtained results for the cell cycle of yeast our approach could be exemplary for the analysis of general pathways by exploiting the rich causal structure of inferred and/or curated gene networks including protein or signaling networks.
基因网络是基因或其产物之间分子相互作用的一种表示形式,因此构成了因果网络。尽管在过去几年中进行了深入研究,但迄今为止,大多数研究都集中在从实验数据重建基因网络的推理方法或其结构特性上,例如度分布。到目前为止,对其进行结构分析以深入了解例如信号通路等组织原则的功能见解仍未得到充分重视。
在本文中,我们分析了酿酒酵母中细胞周期调控基因。我们的分析基于转录调控网络,该网络代表因果相互作用,而不仅仅是基因之间的关联或相关性,以及一份已知的周期性基因列表。未使用其他数据。根据图论性质对转录调控网络进行划分会导致网络中的层次结构,进而导致信息流中的层次结构,从而能够识别出两组周期性基因。这揭示了对细胞周期及其受该途径调控的基因工作机制的一种新颖概念性解释。
除了获得的关于酵母细胞周期的结果外,我们的方法可以作为通过利用推断和/或策划的基因网络(包括蛋白质或信号网络)的丰富因果结构来分析一般途径的范例。