ArticleProceedings of the National Academy of Sciences of the United States of America2017
Modeling the response of a tumor-suppressive network to mitogenic and oncogenic signals.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 30 citations in OpenAlex.
- NO Activates the Triterpenoid Biosynthetic Pathway inInternational journal of molecular sciences · 2025Article
- The network structural entropy for single-cell RNA sequencing data during skin aging.Briefings in bioinformatics · 2024Article
- p53 Orchestrates Cancer Metabolism: Unveiling Strategies to Reverse the Warburg Effect.Bulletin of mathematical biology · 2024Article
- Gallic Acid Hindered Lung Cancer Progression by Inducing Cell Cycle Arrest and Apoptosis in A549 Lung Cancer Cells via PI3K/Akt Pathway.Biomolecules & therapeutics · 2022Article
- Network modeling-based identification of the switching targets between pyroptosis and secondary pyroptosis.Chaos, solitons, and fractals · 2022Article
- Ninety-six-hour starved peripheral blood mononuclear cell supernatant inhibited LA7 breast cancer stem cells induced tumorFrontiers in immunology · 2022Article
- Quantifying the optimal strategy of population control of quorum sensing network in Escherichia coli.NPJ systems biology and applications · 2021Article
- Review
- Downregulation of Stemness Genes and Induction of Necrosis in Rat LA7 Cancer Stem Cells Induced Tumors Treated with Starved Fibroblasts Culture Supernatant.Reports of biochemistry & molecular biology · 2021Article
- Systemic effects of starved fibroblast culture supernatant on immunosuppressed rats treated with cancer stem cells (LA7).Caspian journal of internal medicine · 2020Article
- Mathematical Modeling of p53 Pathways.International journal of molecular sciences · 2019Review
- Coordination of miR-192 and miR-22 in p53-Mediated Cell Fate Decision.International journal of molecular sciences · 2019Article
- Understanding non-linear effects from Hill-type dynamics with application to decoding of p53 signaling.Scientific reports · 2018Article
- Prolonging Gastrointestinal-Stromal-Tumor-free life, an optimal suggestion of imatinib intervention ahead of operation.Journal of Cancer · 2018Article
- A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer.Cancer convergence · 2017Article
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
Intrinsic tumor-suppressive mechanisms protect normal cells against aberrant proliferation. Although cellular signaling pathways engaged in tumor repression have been largely identified, how they are orchestrated to fulfill their function still remains elusive. Here, we built a tumor-suppressive network model composed of three modules responsible for the regulation of cell proliferation, activation of p53, and induction of apoptosis. Numerical simulations show a rich repertoire of network dynamics when normal cells are subject to serum stimulation and adenovirus E1A overexpression. We showed that oncogenic signaling induces ARF and that ARF further promotes p53 activation to inhibit proliferation. Mitogenic signaling activates E2F activators and promotes Akt activation. p53 and E2F1 cooperate to induce apoptosis, whereas Akt phosphorylates p21 to repress caspase activation. These prosurvival and proapoptotic signals compete to dictate the cell fate of proliferation, cell-cycle arrest, or apoptosis. The cellular outcome is also impacted by the kinetic mode (ultrasensitivity or bistability) of p53. When cells are exposed to serum deprivation and recovery under fixed E1A, the shortest starvation time required for apoptosis induction depends on the terminal serum concentration, which was interpreted in terms of the dynamics of caspase-3 activation and cytochrome
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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.