ArticlePLoS computational biology2015
Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed, 150 citations in OpenAlex.
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- Personalized Boolean network dynamic profiling and machine learning approaches to identify combinatorial treatments for prostate cancer.Frontiers in pharmacology · 2026Article
- Uncovering the mechanisms of synergistic drug combinations in non-small cell lung cancer through metagene-based classification.PloS one · 2026Article
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- Constraint based modeling of drug induced metabolic changes in a cancer cell line.NPJ systems biology and applications · 2025Article
- NeKo: A tool for automatic network construction from prior knowledge.PLoS computational biology · 2025Article
- Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.Scientific reports · 2025Article
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- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Refining Boolean models with the partial most permissive scheme.Bioinformatics (Oxford, England) · 2025Article
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- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
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- A Deep Neural Network for Predicting Synergistic Drug Combinations on Cancer.Interdisciplinary sciences, computational life sciences · 2024Article
- Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy.PloS one · 2024Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.