ArticleFrontiers in physiology2020
Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction.
Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.Scientific reports · 2025Article
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy.PloS one · 2024Article
- Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.Frontiers in systems biology · 2023Article
- Synergistic effects of complex drug combinations in colorectal cancer cells predicted by logical modelling.Frontiers in systems biology · 2023Article
- Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance.PLoS computational biology · 2021Article
- A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines.Frontiers in molecular biosciences · 2020Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discrete dynamical modeling shows promise in prioritizing drug combinations for screening efforts by reducing the experimental workload inherent to the vast numbers of possible drug combinations. We have investigated approaches to predict combination responses across different cancer cell lines using logic models generated from one generic prior-knowledge network representing 144 nodes covering major cancer signaling pathways. Cell-line specific models were configured to agree with baseline activity data from each unperturbed cell line. Testing against experimental data demonstrated a high number of true positive and true negative predictions, including also cell-specific responses. We demonstrate the possible enhancement of predictive capability of models by curation of literature knowledge further detailing subtle biologically founded signaling mechanisms in the model topology.
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Registered trials
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.