Cell fate decision network in the AGS gastric cancer cell line (Flobak et al 2015)

Taxon: Mammal  Process: Cancer
Title: Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling
Author(s): Flobak, Åsmund and Baudot, Anaïs and Remy, Elisabeth and Thommesen, Liv and Thieffry, Denis and Kuiper, Martin and Lægreid, Astrid
Journal: PLOS Computational Biology
Year: 2015
DOI: 10.1371/journal.pcbi.1004426

Model file(s)	Description(s)
Flobak_FullModel_S2_Dataset.zginml	Full GINsim model
Flobak_ReducedModel_S3_Dataset.zginml	Reduced GINsim model

Summary:
This model accounts for cell fate decision network in the AGS gastric cancer cell line. A set of logical equations has been defined, wich recapitulates AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. These simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. All predicted non synergic pairs and four of the predicted synergic ones were confirmed in AGS cell growth real-time assays, including known synergic effects of MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions.