Abstract
The Signaling and Dynamic Regulatory Events Miner (SDREM) is a powerful computational approach for identifying which signaling pathways and transcription factors control the temporal cellular response to a stimulus. SDREM builds end-to-end response models by combining condition-independent protein–protein interactions and transcription factor binding data with two types of condition-specific data: source proteins that detect the stimulus and changes in gene expression over time. Here we describe how to apply SDREM to study human diseases, using epidermal growth factor (EGF) response impacting neurogenesis and Alzheimer’s disease as an example.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bar-Joseph Z, Gitter A, Simon I (2012) Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet 13:552–564
Gitter A, Carmi M, Barkai N, Bar-Joseph Z (2013) Linking the signaling cascades and dynamic regulatory networks controlling stress responses. Genome Res 23:365–376
Ernst J, Vainas O, Harbison CT et al (2007) Reconstructing dynamic regulatory maps. Mol Syst Biol 3:74
Schulz MH, Devanny WE, Gitter A et al (2012) DREM 2.0: improved reconstruction of dynamic regulatory networks from time-series expression data. BMC Syst Biol 6:104
Gitter A, Klein-Seetharaman J, Gupta A, Bar-Joseph Z (2011) Discovering pathways by orienting edges in protein interaction networks. Nucleic Acids Res 39:e22
Gitter A, Bar-Joseph Z (2013) Identifying proteins controlling key disease signaling pathways. Bioinformatics 29:i227–i236
Wiltrout C, Lang B, Yan Y et al (2007) Repairing brain after stroke: a review on post-ischemic neurogenesis. Neurochem Int 5:1028–1041
Jin K, Peel AL, Mao XO et al (2004) Increased hippocampal neurogenesis in Alzheimer’s disease. Proc Natl Acad Sci U S A 101:343–347
Repetto E, Yoon I-S, Zheng H, Kang DE (2007) Presenilin 1 regulates epidermal growth factor receptor turnover and signaling in the endosomal-lysosomal pathway. J Biol Chem 282:31504–31516
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
Smoot ME, Ono K, Ruscheinski J et al (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57
Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29
Amit I, Citri A, Shay T et al (2007) A module of negative feedback regulators defines growth factor signaling. Nat Genet 39:503–512
Kanehisa M, Goto S, Sato Y et al (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114
Huang DW, Sherman BT, Stephens R et al (2008) DAVID gene ID conversion tool. Bioinformation 2:428–430
Huang H, McGarvey PB, Suzek BE et al (2011) A comprehensive protein-centric ID mapping service for molecular data integration. Bioinformatics 27:1190–1191
Ernst J, Plasterer HL, Simon I, Bar-Joseph Z (2010) Integrating multiple evidence sources to predict transcription factor binding in the human genome. Genome Res 20:526–536
The ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74
Chatr-aryamontri A, Breitkreutz B-J, Heinicke S et al (2013) The BioGRID interaction database: 2013 update. Nucleic Acids Res 41:D816–D823
Prasad TSK, Goel R, Kandasamy K et al (2009) Human protein reference database—2009 update. Nucleic Acids Res 37:D767–D772
Bader GD, Cary MP, Sander C (2006) Pathguide: a pathway resource list. Nucleic Acids Res 34:D504–D506
Aranda B, Blankenburg H, Kerrien S et al (2011) PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat Meth 8:528–529
Croft D, O’Kelly G, Wu G et al (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39:D691–D697
Acknowledgements
This work was supported by National Institutes of Health (1RO1 GM085022) and National Science Foundation (DBI-0965316) awards to Z.B.J. A.G. is supported by Microsoft Research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Gitter, A., Bar-Joseph, Z. (2016). The SDREM Method for Reconstructing Signaling and Regulatory Response Networks: Applications for Studying Disease Progression. In: Castrillo, J., Oliver, S. (eds) Systems Biology of Alzheimer's Disease. Methods in Molecular Biology, vol 1303. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2627-5_30
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2627-5_30
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-2626-8
Online ISBN: 978-1-4939-2627-5
eBook Packages: Springer Protocols