title: The SDREM Method for Reconstructing Signaling and Regulatory Response Networks: Applications for Studying Disease Progression.
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.
authors.affiliation: Microsoft Research, Cambridge, MA, 02142, USA, gitter@biostat.wisc.edu.
authors.forename: Anthony
authors.initials: A
authors.lastname: Gitter
authors.forename: Ziv
authors.initials: Z
authors.lastname: Bar-Joseph
mesh_terms: D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D018450:Disease Progression; D004815:Epidermal Growth Factor / Q000378:metabolism; D053263:Gene Regulatory Networks; D006801:Humans; D055495:Neurogenesis; D060066:Protein Interaction Maps; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology / Q000379:methods*
chemical_list: D004815:Epidermal Growth Factor