ArticleBiophysical journal2021
Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition.
Article in Biophysical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.Nature communications · 2026Article
- Reconstructing gene network structure and dynamics from single cell data.Bioinformatics (Oxford, England) · 2025Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Transiently increased coordination in gene regulation during cell phenotypic transitions.PRX life · 2024Article
- A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence.NPJ systems biology and applications · 2024Article
- Factors Determining Epithelial-Mesenchymal Transition in Cancer Progression.International journal of molecular sciences · 2024Review
- Data-driven energy landscape reveals critical genes in cancer progression.NPJ systems biology and applications · 2024Article
- Landscape quantifies the intermediate state and transition dynamics in ecological networks.PLoS computational biology · 2024Article
- Systematic analysis and optimization of early warning signals for critical transitions using distribution data.iScience · 2023Article
- Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.Frontiers in network physiology · 2023Review
- Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma.iScience · 2022Article
- Bidirectional crosstalk between epithelial-mesenchymal plasticity and IFNRoyal Society open science · 2022Article
- Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?Current issues in molecular biology · 2022Review
- Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data.PLoS computational biology · 2022Article
- Multiple transcription auto regulatory loops can act as robust oscillators and decision-making motifs.Computational and structural biotechnology journal · 2022Article
- Transcriptomic-Based Quantification of the Epithelial-Hybrid-Mesenchymal Spectrum across Biological Contexts.Biomolecules · 2021Article
- Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning.Frontiers in oncology · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epithelial-mesenchymal transition (EMT), a basic developmental process that might promote cancer metastasis, has been studied from various perspectives. Recently, the early warning theory has been used to anticipate critical transitions in EMT from mathematical modeling. However, the underlying mechanisms of EMT involving complex molecular networks remain to be clarified. Especially, how to quantify the global stability and stochastic transition dynamics of EMT and what the underlying mechanism for early warning theory in EMT is remain to be fully clarified. To address these issues, we constructed a comprehensive gene regulatory network model for EMT and quantified the corresponding potential landscape. The landscape for EMT displays multiple stable attractors, which correspond to E, M, and some other intermediate states. Based on the path-integral approach, we identified the most probable transition paths of EMT, which are supported by experimental data. Correspondingly, the results of transition actions demonstrated that intermediate states can accelerate EMT, consistent with recent studies. By integrating the landscape and path with early warning concept, we identified the potential barrier height from the landscape as a global and more accurate measure for early warning signals to predict critical transitions in EMT. The landscape results also provide an intuitive and quantitative explanation for the early warning theory. Overall, the landscape and path results advance our mechanistic understanding of dynamical transitions and roles of intermediate states in EMT, and the potential barrier height provides a new, to our knowledge, measure for critical transitions and quantitative explanations for the early warning theory.
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