ArticlePhysical chemistry chemical physics : PCCP2016
Quantifying the landscape and kinetic paths for epithelial-mesenchymal transition from a core circuit.
Article in Physical chemistry chemical physics : PCCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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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.
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Who cites it
33 citing papers in PubMed.
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.Nature communications · 2026Article
- Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data.Biophysical journal · 2024Article
- EPR-Net: constructing a non-equilibrium potential landscape via a variational force projection formulation.National science review · 2024Article
- Data- and theory-driven approaches for understanding paths of epithelial-mesenchymal transition.Genesis (New York, N.Y. : 2000) · 2024Review
- Theoretical and computational tools to model multistable gene regulatory networks.Reports on progress in physics. Physical Society (Great Britain) · 2023Review
- Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.Frontiers in network physiology · 2023Review
- Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma.iScience · 2021Article
- Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition.Biophysical journal · 2021Article
- Review
- Computational systems-biology approaches for modeling gene networks driving epithelial-mesenchymal transitions.Computational and systems oncology · 2021Article
- NRF2-dependent Epigenetic Regulation can Promote the Hybrid Epithelial/Mesenchymal Phenotype.Frontiers in cell and developmental biology · 2021Article
- Epigenetic Regulation of Epithelial to Mesenchymal Transition in the Cancer Metastatic Cascade: Implications for Cancer Therapy.Frontiers in oncology · 2021Review
- Quantifying the Landscape and Transition Paths for Proliferation-Quiescence Fate Decisions.Journal of clinical medicine · 2020Article
- Epigenetic feedback and stochastic partitioning during cell division can drive resistance to EMT.Oncotarget · 2020Article
- Understanding the Principles of Pattern Formation Driven by Notch Signaling by Integrating Experiments and Theoretical Models.Frontiers in physiology · 2020Review
- Inference of Intercellular Communications and Multilayer Gene-Regulations of Epithelial-Mesenchymal Transition From Single-Cell Transcriptomic Data.Frontiers in genetics · 2020Article
- A Computational Model of the Endothelial to Mesenchymal Transition.Frontiers in genetics · 2020Article
- Exposing the Underlying Relationship of Cancer Metastasis to Metabolism and Epithelial-Mesenchymal Transitions.iScience · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
Epithelial-mesenchymal transition (EMT), as a crucial process in embryonic development and cancer metastasis, has been investigated extensively. However, how to quantify the global stability and transition dynamics for EMT under fluctuations remains to be elucidated. Starting from a core EMT genetic circuit composed of three key proteins or microRNAs (microRNA-200, ZEB and SNAIL), we uncovered the potential landscape for the EMT process. Three attractors emerge from the landscape, which correspond to epithelial, mesenchymal and partial EMT states respectively. Based on the landscape, we analyzed two important quantities of the EMT system: the barrier heights between different basins of attraction that describe the degree of difficulty for EMT or backward transition, and the mean first passage time (MFPT) that characterizes the kinetic transition rate. These quantities can be harnessed as measurements for the stability of cell types and the degree of difficulty of transitions between different cell types. We also calculated the minimum action paths (MAPs) by path integral approaches. The MAP delineates the transition processes between different cell types quantitatively. We propose two different EMT processes: a direct EMT from E to P, and a step-wise EMT going through an intermediate state, depending on different extracellular environments. The landscape and kinetic paths we acquired offer a new physical and quantitative way for understanding the mechanisms of EMT processes, and indicate the possible roles for the intermediate states.
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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.