Department of Biological and Environmental Studies, University of Sannio, Benevento, I-82100, Italy.
BMC Bioinformatics. 2010 Mar 25;11:154. doi: 10.1186/1471-2105-11-154.
One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory.
In this paper we show how the ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) algorithm can be used for gene regulatory network inference in the case of time-course expression profiles. The resulting method is called TimeDelay-ARACNE. It just tries to extract dependencies between two genes at different time delays, providing a measure of these dependencies in terms of mutual information. The basic idea of the proposed algorithm is to detect time-delayed dependencies between the expression profiles by assuming as underlying probabilistic model a stationary Markov Random Field. Less informative dependencies are filtered out using an auto calculated threshold, retaining most reliable connections. TimeDelay-ARACNE can infer small local networks of time regulated gene-gene interactions detecting their versus and also discovering cyclic interactions also when only a medium-small number of measurements are available. We test the algorithm both on synthetic networks and on microarray expression profiles. Microarray measurements concern S. cerevisiae cell cycle, E. coli SOS pathways and a recently developed network for in vivo assessment of reverse engineering algorithms. Our results are compared with ARACNE itself and with the ones of two previously published algorithms: Dynamic Bayesian Networks and systems of ODEs, showing that TimeDelay-ARACNE has good accuracy, recall and F-score for the network reconstruction task.
Here we report the adaptation of the ARACNE algorithm to infer gene regulatory networks from time-course data, so that, the resulting network is represented as a directed graph. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.
分子生物学的主要目标之一是获得关于分子成分如何相互作用以及理解基因功能调控的知识。使用微阵列技术，可以在单个分析步骤中提取数千个基因的测量值，从而获得细胞基因表达的图像。已经开发了几种从稳态数据推断基因网络的方法，但关于时程数据的文献却很少，因此，开发从时间序列测量推断基因网络的算法是生物信息学研究领域的一个当前挑战。为了检测不同时滞下基因之间的依赖性，我们提出了一种从时间序列测量中推断基因调控网络的方法，该方法从基于信息理论的著名算法开始。
在本文中，我们展示了如何使用 ARACNE（准确细胞网络重建算法）算法来推断时程表达谱中的基因调控网络。所得到的方法称为 TimeDelay-ARACNE。它只是试图提取不同时滞下两个基因之间的依赖性，并用互信息来衡量这些依赖性。该算法的基本思想是通过假设潜在的概率模型为平稳马尔可夫随机场，来检测表达谱之间的时滞依赖性。使用自动计算的阈值过滤掉信息量较少的依赖性，保留最可靠的连接。TimeDelay-ARACNE 可以推断出受时间调控的基因-基因相互作用的小局部网络，检测它们的相互作用，甚至在只有中等数量的测量值可用的情况下也可以发现循环相互作用。我们在合成网络和微阵列表达谱上测试了该算法。微阵列测量涉及酿酒酵母细胞周期、大肠杆菌 SOS 途径和最近开发的用于体内评估反向工程算法的网络。我们将结果与 ARACNE 本身以及两个先前发表的算法进行了比较：动态贝叶斯网络和系统的 ODEs，结果表明，TimeDelay-ARACNE 在网络重建任务中具有良好的准确性、召回率和 F 分数。
在这里，我们报告了将 ARACNE 算法改编为从时程数据推断基因调控网络的方法，因此，生成的网络表示为有向图。所提出的算法有望用于从时程数据重建小型生物有向网络。
