ArticlePLoS computational biology2016
Feedbacks, Bifurcations, and Cell Fate Decision-Making in the p53 System.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 66 citations in OpenAlex.
- Splicing to keep splicing: A feedback system for cellular homeostasis and state transition.Clinical and translational medicine · 2025Review
- ATM and p53 in aging and cancer: a double-edged sword in genomic integrity.Biogerontology · 2025Review
- Visualizing mechanistic models by integrating site-specific molecular details into reaction networks.Frontiers in molecular biosciences · 2025Article
- Basal State Calibration of a Chemical Reaction Network Model for Autophagy.International journal of molecular sciences · 2024Article
- TOPAS-Tissue: A Framework for the Simulation of the Biological Response to Ionizing Radiation at the Multi-Cellular Level.International journal of molecular sciences · 2024Article
- FateNet: an integration of dynamical systems and deep learning for cell fate prediction.Bioinformatics (Oxford, England) · 2024Article
- Predictive power of non-identifiable models.Scientific reports · 2023Article
- Computational analysis of synergism in small networks with different logic.Journal of biological physics · 2023Article
- Modeling of DNA Damage Repair and Cell Response in Relation to p53 System Exposed to Ionizing Radiation.International journal of molecular sciences · 2022Article
- Single-cell analysis of p53 transitional dynamics unravels stimulus- and cell type-dependent signaling output motifs.BMC biology · 2022Article
- Transition and identification of pathological states in p53 dynamics for therapeutic intervention.Scientific reports · 2021Article
- Dual roles of SIRT1 in the BAX switch through the P53 module: A mathematical modeling study.Computational and structural biotechnology journal · 2021Article
- Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning.Frontiers in oncology · 2021Review
- Model-based optimization of combination protocols for irradiation-insensitive cancers.Scientific reports · 2020Article
- PEDL: extracting protein-protein associations using deep language models and distant supervision.Bioinformatics (Oxford, England) · 2020Article
- Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods.Integrative biology : quantitative biosciences from nano to macro · 2020Article
- Parallel Tempering with Lasso for model reduction in systems biology.PLoS computational biology · 2020Article
- Mathematical Modeling of p53 Pathways.International journal of molecular sciences · 2019Review
- Helping the Released Guardian: Drug Combinations for Supporting the Anticancer Activity of HDM2 (MDM2) Antagonists.Cancers · 2019Review
- Cell fate in antiviral response arises in the crosstalk of IRF, NF-κB and JAK/STAT pathways.Nature communications · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The p53 transcription factor is a regulator of key cellular processes including DNA repair, cell cycle arrest, and apoptosis. In this theoretical study, we investigate how the complex circuitry of the p53 network allows for stochastic yet unambiguous cell fate decision-making. The proposed Markov chain model consists of the regulatory core and two subordinated bistable modules responsible for cell cycle arrest and apoptosis. The regulatory core is controlled by two negative feedback loops (regulated by Mdm2 and Wip1) responsible for oscillations, and two antagonistic positive feedback loops (regulated by phosphatases Wip1 and PTEN) responsible for bistability. By means of bifurcation analysis of the deterministic approximation we capture the recurrent solutions (i.e., steady states and limit cycles) that delineate temporal responses of the stochastic system. Direct switching from the limit-cycle oscillations to the "apoptotic" steady state is enabled by the existence of a subcritical Neimark-Sacker bifurcation in which the limit cycle loses its stability by merging with an unstable invariant torus. Our analysis provides an explanation why cancer cell lines known to have vastly diverse expression levels of Wip1 and PTEN exhibit a broad spectrum of responses to DNA damage: from a fast transition to a high level of p53 killer (a p53 phosphoform which promotes commitment to apoptosis) in cells characterized by high PTEN and low Wip1 levels to long-lasting p53 level oscillations in cells having PTEN promoter methylated (as in, e.g., MCF-7 cell line).
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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.