Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

PLoS Computational Biology
Åsmund FlobakAstrid Lægreid

Abstract

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating 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. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhi...Continue Reading

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Citations

Feb 24, 2016·Frontiers in Bioengineering and Biotechnology·Bhanwar Lal PuniyaTomáš Helikar
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Methods Mentioned

BETA
xenograft
ubiquitination
FCS

Software Mentioned

CellDesigner
DREAM
GINSIM
ZGINML
CompuSyn

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