Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies

BMC Bioinformatics

Methodology Article

Anne Siegel¹  Luca Cerone²  Julio Saez-Rodriguez²  Sven Thiele³  Steffen Klamt³  Carito Guziołowski⁴ 

[1] CNRS, UMR 6074 IRISA, Campus de Beaulieu, 35042, Rennes, France;INRIA, Dyliss project, Campus de Beaulieu, 35042, Rennes, France;European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB101SD, UK;Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany;École Centrale de Nantes, IRCCyN UMR 6597, 1 rue de la Noë, 44321, Nantes, France;

关键词: E. coli;    Gene regulation;    Interaction graphs;    Sign consistency;    Uncertainty;    Logic modeling;    Answer Set Programming (ASP);   

DOI  :  10.1186/s12859-015-0733-7

 received in 2015-04-30, accepted in 2015-09-09,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundA 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.ResultsWe 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.ConclusionOverall, 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

【 授权许可】

CC BY   
© Thiele et al. 2015