title: Construction of cell type-specific logic models of signaling networks using CellNOpt.
abstract: Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.
authors.affiliation: Center for Cell Decision Processes Massachusetts Institute of Technology and Harvard Medical School, Cambridge, MA, USA.
authors.forename: Melody K
authors.initials: MK
authors.lastname: Morris
authors.forename: Ioannis
authors.initials: I
authors.lastname: Melas
authors.forename: Julio
authors.initials: J
authors.lastname: Saez-Rodriguez
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D017143:Fuzzy Logic; D056945:Hep G2 Cells; D006801:Humans; D008954:Models, Biological; D009928:Organ Specificity; D015398:Signal Transduction