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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 7686081, 11 pages
http://dx.doi.org/10.1155/2016/7686081
Research Article

Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection

1Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY 14642, USA
2Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA

Received 20 October 2015; Accepted 24 December 2015

Academic Editor: Chuangyin Dang

Copyright © 2016 Christopher S. Anderson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets) have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV) infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC) infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection.