INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, Rennes 35042 France.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Sep-Oct;8(5):1223-34. doi: 10.1109/TCBB.2010.71.
We discuss the propagation of constraints in eukaryotic interaction networks in relation to model prediction and the identification of critical pathways. In order to cope with posttranslational interactions, we consider two types of nodes in the network, corresponding to proteins and to RNA. Microarray data provides very lacunar information for such types of networks because protein nodes, although needed in the model, are not observed. Propagation of observations in such networks leads to poor and nonsignificant model predictions, mainly because rules used to propagate information--usually disjunctive constraints--are weak. Here, we propose a new, stronger type of logical constraints that allow us to strengthen the analysis of the relation between microarray and interaction data. We use these rules to identify the nodes which are responsible for a phenotype, in particular for cell cycle progression. As the benchmark, we use an interaction network describing major pathways implied in Ewing's tumor development. The Python library used to obtain our results is publicly available on our supplementary web page.
我们讨论了真核相互作用网络中约束的传播与模型预测和关键途径识别的关系。为了处理翻译后相互作用，我们在网络中考虑了两种类型的节点，分别对应于蛋白质和 RNA。微阵列数据为这类网络提供了非常有限的信息，因为尽管模型中需要蛋白质节点，但它们是无法观察到的。在这样的网络中，观察结果的传播会导致模型预测效果不佳且不显著，主要是因为用于传播信息的规则——通常是离散约束——比较弱。在这里，我们提出了一种新的、更强类型的逻辑约束，使我们能够加强微阵列和相互作用数据之间关系的分析。我们使用这些规则来确定负责表型的节点，特别是细胞周期进展的节点。作为基准，我们使用描述尤文氏瘤发展中涉及的主要途径的相互作用网络。用于获得结果的 Python 库可在我们的补充网页上公开获得。
