Department of Biological and Environmental Sciences, University of Sannio, Benevento, Italy.
BMC Bioinformatics. 2009 Dec 23;10:444. doi: 10.1186/1471-2105-10-444.
The ultimate aim of systems biology is to understand and describe how molecular components interact to manifest collective behaviour that is the sum of the single parts. Building a network of molecular interactions is the basic step in modelling a complex entity such as the cell. Even if gene-gene interactions only partially describe real networks because of post-transcriptional modifications and protein regulation, using microarray technology it is possible to combine measurements for thousands of genes into a single analysis step that provides a picture of the cell's gene expression. Several databases provide information about known molecular interactions and various methods have been developed to infer gene networks from expression data. However, network topology alone is not enough to perform simulations and predictions of how a molecular system will respond to perturbations. Rules for interactions among the single parts are needed for a complete definition of the network behaviour. Another interesting question is how to integrate information carried by the network topology, which can be derived from the literature, with large-scale experimental data.
Here we propose an algorithm, called inference of regulatory interaction schema (IRIS), that uses an iterative approach to map gene expression profile values (both steady-state and time-course) into discrete states and a simple probabilistic method to infer the regulatory functions of the network. These interaction rules are integrated into a factor graph model. We test IRIS on two synthetic networks to determine its accuracy and compare it to other methods. We also apply IRIS to gene expression microarray data for the Saccharomyces cerevisiae cell cycle and for human B-cells and compare the results to literature findings.
IRIS is a rapid and efficient tool for the inference of regulatory relations in gene networks. A topological description of the network and a matrix of gene expression profiles are required as input to the algorithm. IRIS maps gene expression data onto discrete values and then computes regulatory functions as conditional probability tables. The suitability of the method is demonstrated for synthetic data and microarray data. The resulting network can also be embedded in a factor graph model.
系统生物学的最终目标是理解和描述分子组件如何相互作用,从而表现出整体行为,即各部分的总和。构建分子相互作用网络是对细胞等复杂实体进行建模的基本步骤。即使由于转录后修饰和蛋白质调节,基因-基因相互作用仅部分描述了真实网络,但使用微阵列技术,我们可以将数千个基因的测量值组合到单个分析步骤中,从而提供细胞基因表达的图片。有几个数据库提供了有关已知分子相互作用的信息,并且已经开发了各种方法来从表达数据推断基因网络。然而,仅网络拓扑结构不足以模拟和预测分子系统对干扰的响应。需要交互规则来完整定义网络行为。另一个有趣的问题是如何将网络拓扑结构(可以从文献中得出)携带的信息与大规模实验数据进行整合。
在这里,我们提出了一种称为推断调节相互作用模式(IRIS)的算法,该算法使用迭代方法将基因表达谱值(稳态和时程)映射到离散状态,并使用简单的概率方法推断网络的调节功能。这些交互规则被集成到因子图模型中。我们在两个合成网络上测试 IRIS 以确定其准确性,并将其与其他方法进行比较。我们还将 IRIS 应用于酿酒酵母细胞周期和人类 B 细胞的基因表达微阵列数据,并将结果与文献发现进行比较。
IRIS 是推断基因网络中调节关系的快速有效的工具。网络的拓扑描述和基因表达谱矩阵是算法的输入。IRIS 将基因表达数据映射到离散值,然后计算作为条件概率表的调节功能。该方法适用于合成数据和微阵列数据。生成的网络也可以嵌入因子图模型中。