Thiele Sven, Cerone Luca, Saez-Rodriguez Julio, Siegel Anne, Guziołowski Carito, Klamt Steffen
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, 39106, Germany.
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB101SD, UK.
BMC Bioinformatics. 2015 Oct 28;16:345. doi: 10.1186/s12859-015-0733-7.
A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions.
We present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles. We furthermore introduce a new constraint to filter undesired model behaviors induced by positive feedback loops. Finally, we generalize the way predictions can be made by the sign consistency approach. In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach. We then demonstrate the applicability of our framework by confronting a large-scale gene regulatory network model of Escherichia coli with high-throughput transcriptomic measurements.
Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks.
在诸如KEGG、Reactome或RegulonDB等数据库中，关于信号传导和基因调控网络的知识量正在迅速增长。越来越需要将这些知识与高通量数据相关联，以便验证或否定网络拓扑结构，或者确定在特定环境条件下给定细胞类型中哪些相互作用存在或不存在。相互作用图提供了一种适合表示具有信息流的细胞网络的方式，并且基于符号一致性方法的方法已被证明是有价值的工具，可用于：（i）预测定性反应；（ii）测试网络拓扑结构和实验数据的一致性；（iii）对网络模型应用修复操作，以提示缺失或错误的相互作用。
我们提出了一个框架，以统一不同的符号一致性概念，并提出了一种改进的数据离散化方法，该方法考虑了实验数据中的不确定性。我们还引入了一个新的约束条件，以过滤由正反馈回路引起的不期望的模型行为。最后，我们推广了通过符号一致性方法进行预测的方式。特别是，我们区分了强预测（例如节点水平的增加）和弱预测（例如节点水平增加或保持不变），从而扩大了该方法的整体预测能力。然后，我们通过将大肠杆菌的大规模基因调控网络模型与高通量转录组测量数据进行对比，展示了我们框架的适用性。
总体而言，我们的工作增强了符号一致性方法在预测信号传导和基因调控网络行为方面的灵活性和能力，更广泛地说，增强了在验证和推断这些网络方面的灵活性和能力。
