School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
Bioinformatics. 2011 Oct 15;27(20):2873-9. doi: 10.1093/bioinformatics/btr487. Epub 2011 Sep 7.
A knowledge of the dynamics of transcription factors is fundamental to understand the transcriptional regulation mechanism. Nowadays, an experimental measure of transcription factor activities in vivo represents a challenge. Several methods have been developed to infer these activities from easily measurable quantities such as mRNA expression of target genes. A limitation of these methods is represented by the fact that they rely on very simple single-layer structures, typically consisting of one or more transcription factors regulating a number of target genes.
We present a novel statistical inference methodology to reverse engineer the dynamics of transcription factors in hierarchical network motifs such as feed-forward loops. The approach we present is based on a continuous time representation of the system where the high-level master transcription factor is represented as a two state Markov jump process driving a system of differential equations. We solve the inference problem using an efficient variational approach and demonstrate our method on simulated data and two real datasets. The results on real data show that the predictions of our approach can capture biological behaviours in a more effective way than single-layer models of transcription, and can lead to novel biological insights.
http://homepages.inf.ed.ac.uk/gsanguin/software.html
Supplementary data are available at Bioinformatics online.
了解转录因子的动力学对于理解转录调控机制至关重要。如今，在体内测量转录因子活性是一项挑战。已经开发了几种方法来从易于测量的数量（例如靶基因的 mRNA 表达）推断这些活性。这些方法的一个局限性在于它们依赖于非常简单的单层结构，通常由一个或多个转录因子调节多个靶基因组成。
我们提出了一种新的统计推断方法，用于反向工程转录因子在层次网络基序（如前馈环）中的动态。我们提出的方法基于系统的连续时间表示，其中高级主转录因子表示为驱动微分方程系统的二态马尔可夫跳跃过程。我们使用有效的变分方法解决推断问题，并在模拟数据和两个真实数据集上演示我们的方法。真实数据的结果表明，与转录的单层模型相比，我们方法的预测可以更有效地捕获生物学行为，并可以带来新的生物学见解。
http://homepages.inf.ed.ac.uk/gsanguin/software.html
补充数据可在 Bioinformatics 在线获得。
