title: A data-driven computational model of the ErbB receptor signaling network.
abstract: Signal transduction networks are often described by qualitative models that incorporate experimental findings from different cell types. However, a large body of evidence suggests that cell signaling differs substantially from one cell type to the next. Even in cases in which the topology of signaling networks is conserved, different cell types respond differently under the same experimental conditions. In this work we address the problem of developing quantitative and cell-type specific models of cells signaling that incorporate experimental data obtained from protein arrays and mathematical modeling. As a biological system we examine signaling mediated ErbB receptor family as the current literature is very controversial.
authors.affiliation: Merrimack Pharmaceuticals, Cambridge, MA 02141, USA.
authors.forename: Birgit
authors.initials: B
authors.lastname: Schoeberl
authors.forename: Emily
authors.initials: E
authors.lastname: Pace
authors.forename: Shavonne
authors.initials: S
authors.lastname: Howard
authors.forename: Viara
authors.initials: V
authors.lastname: Garantcharova
authors.forename: Art
authors.initials: A
authors.lastname: Kudla
authors.forename: Peter K
authors.initials: PK
authors.lastname: Sorger
authors.forename: Ulrik B
authors.initials: UB
authors.lastname: Nielsen
mesh_terms: D003198:Computer Simulation; D016208:Databases, Factual; D004815:Epidermal Growth Factor / Q000378:metabolism*; D066246:ErbB Receptors / Q000378:metabolism*; D008954:Models, Biological; D011505:Protein-Tyrosine Kinases / Q000378:metabolism*; D015398:Signal Transduction / Q000187:drug effects / Q000502:physiology*
chemical_list: D004815:Epidermal Growth Factor; D066246:ErbB Receptors; D011505:Protein-Tyrosine Kinases