Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies
Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies
复制标题
DOI：
10.1186/s12859-015-0733-7
复制
发表时间：
2015-10-28
影响因子：
3
通讯作者：
Klamt, Steffen
中科院分区：
文献类型：
--
作者：
Thiele, Sven;Cerone, Luca;Klamt, Steffen
背景资料：关于信号传导和基因调控网络的知识在KEGG，Reactome或RegulonDB等数据库中快速增长。有一个不断增长的需要，以相关的知识，以高吞吐量的数据，以（中）验证网络拓扑结构或决定哪些相互作用是存在的或在特定的环境条件下，在给定的细胞类型不活跃。交互图提供了一个合适的表示蜂窝网络的信息流和方法的基础上签署一致性的方法已被证明是有价值的工具（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）适用于修复操作的网络模型，建议丢失或错误的相互作用。我们提出了一个框架，统一不同的概念，标志的一致性，并提出了一个改进的方法，考虑实验配置文件中的不确定性数据离散化。此外，我们引入了一个新的约束过滤不希望的模型行为引起的正反馈回路。最后，我们概括的方式可以通过符号一致性的方法进行预测。特别地，我们区分强预测（例如，节点级别的增加）和弱预测（例如，节点级别增加或保持不变），从而扩大了该方法的总体预测能力。然后，我们通过面对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来证明我们的框架的适用性。结论：总体而言，我们的工作增强了符号一致性方法的灵活性和能力，用于预测信号和基因调控网络的行为，更普遍地，用于这些网络的验证和推断
Background: 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.Results: 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.Conclusion: 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