Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka 565-0871, Japan.
Gene. 2013 Apr 10;518(1):17-25. doi: 10.1016/j.gene.2012.11.090. Epub 2012 Dec 21.
Identifying the differences between gene regulatory networks under varying biological conditions or external stimuli is an important challenge in systems biology. Several methods have been developed to reverse-engineer a cellular system, called a gene regulatory network, from gene expression profiles in order to understand transcriptomic behavior under various conditions of interest. Conventional methods infer the gene regulatory network independently from each of the multiple gene expression profiles under varying conditions to find the important regulatory relations for understanding cellular behavior. However, the inferred networks with conventional methods include a large number of misleading relations, and the accuracy of the inference is low. This is because conventional methods do not consider other related conditions, and the results of conventional methods include considerable noise due to the limited number of observation points in each expression profile of interest.
We propose a more accurate method for estimating key gene regulatory networks for understanding cellular behavior under various conditions. Our method utilizes multiple gene expression profiles that compose a tree structure under varying conditions. The root represents the original cellular state, and the leaves represent the changed cellular states under various conditions. By using this tree-structured gene expression profiles, our method more powerfully estimates the networks that are key to understanding the cellular behavior of interest under varying conditions.
We confirmed that the proposed method in cell differentiation was more rigorous than the conventional method. The results show that our assumptions as to which relations are unimportant for understanding the differences of cellular states in cell differentiation are appropriate, and that our method can infer more accurately the core networks of the cell types.
在不同的生物条件或外部刺激下识别基因调控网络的差异是系统生物学中的一个重要挑战。已经开发了几种方法来从基因表达谱中反向工程细胞系统，称为基因调控网络，以了解各种感兴趣条件下的转录组行为。传统方法从多个基因表达谱中的每一个独立推断基因调控网络，以找到理解细胞行为的重要调控关系。然而，传统方法推断的网络包含大量误导性关系，并且推断的准确性较低。这是因为传统方法没有考虑其他相关条件，并且由于每个感兴趣的表达谱中的观察点数量有限，传统方法的结果包括相当多的噪声。
我们提出了一种更准确的方法来估计关键基因调控网络，以了解各种条件下的细胞行为。我们的方法利用了在不同条件下构成树结构的多个基因表达谱。根代表原始细胞状态，叶子代表各种条件下的细胞状态变化。通过使用这种树状基因表达谱，我们的方法更有力地估计了理解各种条件下感兴趣的细胞行为的关键网络。
我们在细胞分化中证实，所提出的方法比传统方法更严格。结果表明，我们对理解细胞状态差异的关系不重要的假设是合适的，并且我们的方法可以更准确地推断细胞类型的核心网络。
