ReviewCurrent opinion in systems biology2018
Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer.
Review in Current opinion in systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- CanSeer: a translational methodology for developing personalized cancer models and therapeutics.Scientific reports · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Modulating the dynamics of NFκB and PI3K enhances the ensemble-level TNFR1 signaling mediated apoptotic response.NPJ systems biology and applications · 2023Article
- Reversing pathological cell states: the road less travelled can extend the therapeutic horizon.Trends in cell biology · 2023Review
- Reducing Boolean networks with backward equivalence.BMC bioinformatics · 2023Article
- Influence maximization in Boolean networks.Nature communications · 2022Article
- Effective connectivity determines the critical dynamics of biochemical networks.Journal of the Royal Society, Interface · 2022Article
- Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells.Computational and structural biotechnology journal · 2022Review
- Can Systems Biology Advance Clinical Precision Oncology?Cancers · 2021Review
- Cell Line-Specific Network Models of ERCancer research · 2021Article
- A systematic analysis of signaling reactivation and drug resistance.Cell reports · 2021Article
- In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer.International journal of molecular sciences · 2021Review
- Identification of disease treatment mechanisms through the multiscale interactome.Nature communications · 2021Article
- CLARINET: efficient learning of dynamic network models from literature.Bioinformatics advances · 2021Article
- Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response.Frontiers in immunology · 2021Review
- Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies.Cancers · 2020Article
- Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies.Molecular systems biology · 2020Article
- Article
- A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint.Scientific reports · 2019Article
- Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition.Physical biology · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.
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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.