title: An approach for optimally extending mathematical models of signaling networks using omics data.
abstract: Mathematical modeling is a key process in Systems Biology and the use of computational tools such as Cytoscape for omics data processing, need to be integrated in the modeling activity. In this paper we propose a new methodology for modeling signaling networks by combining ordinary differential equation models and a gene recommender system, GeneMANIA. We started from existing models, that are stored in the BioModels database, and we generated a query to use as input for the GeneMANIA algorithm. The output of the recommender system was then led back to the kinetic reactions that were finally added to the starting model. We applied the proposed methodology to EGFR-IGF1R signal transduction network, which plays an important role in translational oncology and cancer therapy of non small cell lung cancer.
authors.forename: Fortunato
authors.initials: F
authors.lastname: Bianconi
authors.forename: Federico
authors.initials: F
authors.lastname: Patiti
authors.forename: Elisa
authors.initials: E
authors.lastname: Baldelli
authors.forename: Lucio
authors.initials: L
authors.lastname: Crino
authors.forename: Paolo
authors.initials: P
authors.lastname: Valigi
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000378:metabolism; D003198:Computer Simulation; D016208:Databases, Factual; D066246:ErbB Receptors / Q000378:metabolism; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008175:Lung Neoplasms / Q000378:metabolism; D008962:Models, Theoretical; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
chemical_list: C512478:EGFR protein, human; D066246:ErbB Receptors