ArticleCancer convergence2017
A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer.
Article in Cancer convergence, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 59 citations in OpenAlex.
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- Petri net on expanded networks computes the dynamics of finite multilevel models.Interface focus · 2025Article
- 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
- CanSeer: a translational methodology for developing personalized cancer models and therapeutics.Scientific reports · 2025Article
- Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers.Endocrine oncology (Bristol, England) · 2025Review
- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
- Boolean modeling of breast cancer signaling pathways uncovers mechanisms of drug synergy.PloS one · 2024Article
- Emergent dynamics of underlying regulatory network links EMT and androgen receptor-dependent resistance in prostate cancer.Computational and structural biotechnology journal · 2023Article
- NETISCE: a network-based tool for cell fate reprogramming.NPJ systems biology and applications · 2022Article
- Influence maximization in Boolean networks.Nature communications · 2022Article
- A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach.Cell proliferation · 2022Article
- Article
- Effective connectivity determines the critical dynamics of biochemical networks.Journal of the Royal Society, Interface · 2022Article
- Cell Line-Specific Network Models of ERCancer research · 2021Article
- A practical guide to mechanistic systems modeling in biology using a logic-based approach.Briefings in bioinformatics · 2021Review
- Article
- Breast cancer dormancy: need for clinically relevant models to address current gaps in knowledge.NPJ breast cancer · 2021Review
- The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network.
resultsHere we present a comprehensive network, and discrete dynamic model, of signal transduction in ER+ breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone.
conclusionsThe use of a logic-based, discrete dynamic model enables the identification of results that are mainly due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.
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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.