Signaling hypergraphs.

Signaling pathways function as the information-passing mechanisms of cells. A number of databases with extensive manual curation represent the current knowledge base for signaling pathways. These databases motivate the development of computational approaches for prediction and analysis. Such methods require an accurate and computable representation of signaling pathways. Pathways are often described as sets of proteins or as pairwise interactions between proteins. However, many signaling mechanisms cannot be described using these representations. In this opinion, we highlight a representation of signaling pathways that is underutilized: the hypergraph. We demonstrate the usefulness of hypergraphs in this context and discuss challenges and opportunities for the scientific community.

[1]  D. Lauffenburger,et al.  Physicochemical modelling of cell signalling pathways , 2006, Nature Cell Biology.

[2]  Karin Breuer,et al.  InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation , 2012, Nucleic Acids Res..

[3]  Zhenjun Hu,et al.  Towards zoomable multidimensional maps of the cell , 2007, Nature Biotechnology.

[4]  T. M. Murali,et al.  Top-Down Network Analysis to Drive Bottom-Up Modeling of Physiological Processes , 2013, J. Comput. Biol..

[5]  Atul J. Butte,et al.  Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges , 2012, PLoS Comput. Biol..

[6]  M. Vidal,et al.  Literature-curated protein interaction datasets , 2009, Nature Methods.

[7]  Alexandre P. Francisco,et al.  TFRank: network-based prioritization of regulatory associations underlying transcriptional responses , 2011, Bioinform..

[8]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Mark A. Ragan,et al.  Extracting reaction networks from databases–opening Pandora’s box , 2013, Briefings Bioinform..

[10]  E. Sonnhammer,et al.  Statistical Assessment of Crosstalk Enrichment between Gene Groups in Biological Networks , 2013, PloS one.

[11]  Guy Karlebach,et al.  Modelling and analysis of gene regulatory networks , 2008, Nature Reviews Molecular Cell Biology.

[12]  C. Borgs,et al.  Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem , 2012, J. Comput. Biol..

[13]  Julio Saez-Rodriguez,et al.  Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks , 2012, BMC Systems Biology.

[14]  Xiao-Fan Wang,et al.  Signaling cross-talk between TGF-β/BMP and other pathways , 2009, Cell Research.

[15]  Xuerui Yang,et al.  An Extensive MicroRNA-Mediated Network of RNA-RNA Interactions Regulates Established Oncogenic Pathways in Glioblastoma , 2011, Cell.

[16]  S. Klamt,et al.  Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks , 2013, Cell Communication and Signaling.

[17]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[18]  Steffen Klamt,et al.  A methodology for the structural and functional analysis of signaling and regulatory networks , 2006, BMC Bioinformatics.

[19]  R. Shamir,et al.  Refinement and expansion of signaling pathways: the osmotic response network in yeast. , 2007, Genome research.

[20]  Roded Sharan,et al.  ANAT: A Tool for Constructing and Analyzing Functional Protein Networks , 2011, Science Signaling.

[21]  Steffen Klamt,et al.  Hypergraphs and Cellular Networks , 2009, PLoS Comput. Biol..

[22]  A. Capobianco,et al.  Notch signalling in solid tumours: a little bit of everything but not all the time , 2011, Nature Reviews Cancer.

[23]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[24]  A. Bauer-Mehren,et al.  Pathway databases and tools for their exploitation: benefits, current limitations and challenges , 2009, Molecular systems biology.

[25]  Peter N. Robinson,et al.  GOing Bayesian: model-based gene set analysis of genome-scale data , 2010, Nucleic acids research.

[26]  E. Zotenko,et al.  Inferring Physical Protein Contacts from Large-Scale Purification Data of Protein Complexes* , 2011, Molecular & Cellular Proteomics.

[27]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[28]  Ziv Bar-Joseph,et al.  A Network-based Approach for Predicting Missing Pathway Interactions , 2012, PLoS Comput. Biol..

[29]  David Tuck,et al.  A hyper-graph approach for analyzing transcriptional networks in breast cancer , 2010, BCB '10.

[30]  Claude Berge,et al.  Hypergraphs - combinatorics of finite sets , 1989, North-Holland mathematical library.

[31]  Toshihisa Takagi,et al.  Knowledge representation of signal transduction pathways , 2001, Bioinform..

[32]  Daniele Frigioni,et al.  Directed Hypergraphs: Problems, Algorithmic Results, and a Novel Decremental Approach , 2001, ICTCS.

[33]  TaeHyun Hwang,et al.  A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge , 2009, Bioinform..

[34]  Hassane Alla,et al.  Discrete, continuous, and hybrid Petri Nets , 2004 .

[35]  Simon Kasif,et al.  Biological Process Linkage Networks , 2009, PloS one.

[36]  David Warde-Farley,et al.  GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function , 2008, Genome Biology.

[37]  Giorgio Gallo,et al.  Directed Hypergraphs and Applications , 1993, Discret. Appl. Math..

[38]  T. M. Murali,et al.  Reverse Engineering Molecular Hypergraphs , 2013, TCBB.

[39]  S. Drăghici,et al.  Analysis and correction of crosstalk effects in pathway analysis , 2013, Genome research.

[40]  Jens Nielsen,et al.  Reconstruction and logical modeling of glucose repression signaling pathways in Saccharomyces cerevisiae , 2009, BMC Systems Biology.

[41]  D. Koller,et al.  Automated identification of pathways from quantitative genetic interaction data , 2010, Molecular systems biology.

[42]  Illés J. Farkas,et al.  Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery , 2010, Bioinform..

[43]  David Haussler,et al.  Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM , 2010, Bioinform..

[44]  Allan A. Sioson,et al.  Semantics of Multimodal Network Models , 2009, IEEE ACM Trans. Comput. Biol. Bioinform..

[45]  Luay Nakhleh,et al.  Properties of metabolic graphs: biological organization or representation artifacts? , 2011, BMC Bioinformatics.

[46]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[47]  R. Sharan,et al.  Network-based prediction of protein function , 2007, Molecular systems biology.

[48]  Ron Shamir,et al.  SPIKE: a database of highly curated human signaling pathways , 2010, Nucleic Acids Res..