School of Computer Science, Hebrew University, Jerusalem 91904, Israel.
Bioinformatics. 2011 Jul 1;27(13):i177-85. doi: 10.1093/bioinformatics/btr222.
Deciphering the complex mechanisms by which regulatory networks control gene expression remains a major challenge. While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.
Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation. Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators. Finally, we show the power of Physical Module Networks to reconstruct meaningful regulatory pathways in the genetically perturbed yeast and during the yeast cell cycle, as well as during the response of primary epithelial human cells to infection with H1N1 influenza.
The PMN software is available, free for academic use at http://www.compbio.cs.huji.ac.il/PMN/.
破译调控网络控制基因表达的复杂机制仍然是一个主要挑战。虽然有些研究从假定调控因子的表达水平与其靶标之间的依赖关系推断调控，但其他研究则侧重于测量物理相互作用。
在这里，我们提出了物理模块网络（PMN），这是一个统一的框架，它结合了一个描述共表达基因模块及其共享调控程序的贝叶斯模型，以及一个描述蛋白质-蛋白质相互作用和蛋白质-DNA 结合事件的物理相互作用图，这些事件一致地下调了这种调控。使用合成数据，我们证明了与简单的模块网络相比，物理模块网络模型具有相似的召回率和更高的精度，因为它排除了许多假阳性的调控因子。最后，我们展示了物理模块网络在遗传扰动酵母和酵母细胞周期、以及原发性人上皮细胞对 H1N1 流感感染的反应中重建有意义的调控途径的能力。
PMN 软件可在 http://www.compbio.cs.huji.ac.il/PMN/ 免费供学术使用。
