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This article is part of the supplement: Selected articles from the 10th International Conference on Artificial Immune Systems (ICARIS)

Open Access Proceedings

Reconstruction of regulatory networks through temporal enrichment profiling and its application to H1N1 influenza viral infection

Elena Zaslavsky1*, German Nudelman1, Susanna Marquez24, Uri Hershberg25, Boris M Hartmann1, Juilee Thakar2, Stuart C Sealfon1 and Steven H Kleinstein23*

Author Affiliations

1 Center for Translational Systems Biology and Department of Neurology, Mount Sinai School of Medicine, New York, NY 10029, USA

2 Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA

3 Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA

4 Department of Biology, York University, Toronto, Ontario, Canada M3J 1P3

5 School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA

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BMC Bioinformatics 2013, 14(Suppl 6):S1  doi:10.1186/1471-2105-14-S6-S1

Published: 17 April 2013



H1N1 influenza viruses were responsible for the 1918 pandemic that caused millions of deaths worldwide and the 2009 pandemic that caused approximately twenty thousand deaths. The cellular response to such virus infections involves extensive genetic reprogramming resulting in an antiviral state that is critical to infection control. Identifying the underlying transcriptional network driving these changes, and how this program is altered by virally-encoded immune antagonists, is a fundamental challenge in systems immunology.


Genome-wide gene expression patterns were measured in human monocyte-derived dendritic cells (DCs) infected in vitro with seasonal H1N1 influenza A/New Caledonia/20/1999. To provide a mechanistic explanation for the timing of gene expression changes over the first 12 hours post-infection, we developed a statistically rigorous enrichment approach integrating genome-wide expression kinetics and time-dependent promoter analysis. Our approach, TIme-Dependent Activity Linker (TIDAL), generates a regulatory network that connects transcription factors associated with each temporal phase of the response into a coherent linked cascade. TIDAL infers 12 transcription factors and 32 regulatory connections that drive the antiviral response to influenza. To demonstrate the generality of this approach, TIDAL was also used to generate a network for the DC response to measles infection. The software implementation of TIDAL is freely available at webcite.


We apply TIDAL to reconstruct the transcriptional programs activated in monocyte-derived human dendritic cells in response to influenza and measles infections. The application of this time-centric network reconstruction method in each case produces a single transcriptional cascade that recapitulates the known biology of the response with high precision and recall, in addition to identifying potentially novel antiviral factors. The ability to reconstruct antiviral networks with TIDAL enables comparative analysis of antiviral responses, such as the differences between pandemic and seasonal influenza infections.