title: Insulin Signaling in Insulin Resistance States and Cancer: A Modeling Analysis.
abstract: Insulin resistance is the common denominator of several diseases including type 2 diabetes and cancer, and investigating the mechanisms responsible for insulin signaling impairment is of primary importance. A mathematical model of the insulin signaling network (ISN) is proposed and used to investigate the dose-response curves of components of this network. Experimental data of C2C12 myoblasts with phosphatase and tensin homologue (PTEN) suppressed and data of L6 myotubes with induced insulin resistance have been analyzed by the model. We focused particularly on single and double Akt phosphorylation and pointed out insulin signaling changes related to insulin resistance. Moreover, a new characterization of the upstream signaling of the mammalian target of rapamycin complex 2 (mTORC2) is presented. As it is widely recognized that ISN proteins have a crucial role also in cell proliferation and death, the ISN model was linked to a cell population model and applied to data of a cell line of acute myeloid leukemia treated with a mammalian target of rapamycin inhibitor with antitumor activity. The analysis revealed simple relationships among the concentrations of ISN proteins and the parameters of the cell population model that characterize cell cycle progression and cell death.
authors.affiliation: Institute of Systems Analysis and Computer Science "A. Ruberti", CNR, 00185, Rome, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Bertuzzi
authors.affiliation: Institute of Systems Analysis and Computer Science "A. Ruberti", CNR, 00185, Rome, Italy.|Department of Computer and System Science, Sapienza University of Rome, 00185, Rome, Italy.
authors.forename: Federica
authors.initials: F
authors.lastname: Conte
authors.affiliation: Department of Internal Medicine, Catholic University School of Medicine, 00168, Rome, Italy.
authors.forename: Geltrude
authors.initials: G
authors.lastname: Mingrone
authors.affiliation: Institute of Systems Analysis and Computer Science "A. Ruberti", CNR, 00185, Rome, Italy.|SYSBIO - Centre of Systems Biology, Milan, Italy.
authors.forename: Federico
authors.initials: F
authors.lastname: Papa
authors.affiliation: Department of Computer and System Science, Sapienza University of Rome, 00185, Rome, Italy.
authors.forename: Serenella
authors.initials: S
authors.lastname: Salinari
authors.affiliation: Institute of Systems Analysis and Computer Science "A. Ruberti", CNR, 00185, Rome, Italy.
authors.forename: Carmela
authors.identifier: 0000-0002-5823-7313
authors.initials: C
authors.lastname: Sinisgalli
mesh_terms: D000818:Animals; D002460:Cell Line; D045744:Cell Line, Tumor; D006801:Humans; D007328:Insulin / Q000378:metabolism*; D007333:Insulin Resistance; D051379:Mice; D008962:Models, Theoretical; D018485:Muscle Fibers, Skeletal / Q000378:metabolism; D009369:Neoplasms / Q000378:metabolism*; D010766:Phosphorylation; D051057:Proto-Oncogene Proteins c-akt / Q000378:metabolism; D015398:Signal Transduction; D058570:TOR Serine-Threonine Kinases / Q000037:antagonists & inhibitors / Q000378:metabolism
chemical_list: D007328:Insulin; C546842:MTOR protein, human; D051057:Proto-Oncogene Proteins c-akt; D058570:TOR Serine-Threonine Kinases