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Updated: Jan 22, 2026
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Induction and Analysis of Epithelial to Mesenchymal Transition
Published on: August 27, 2013
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Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition
Vimalathithan Devaraj1, Biplab Bose2
1Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.
Journal of Clinical Medicine
|June 29, 2019
Summary
Investigating Epidermal Growth Factor (EGF)-induced Epithelial to Mesenchymal Transition (EMT) in breast cancer cells reveals reversible state changes. These dynamics are controlled by EGF dose and phosphorylated EGF receptor (EGFR) levels, offering new insights into cell plasticity.
Area of Science:
- Cell biology
- Biophysics
- Cancer research
Background:
- Epithelial to Mesenchymal Transition (EMT) is a complex biological process.
- Phenotypic plasticity is crucial in cancer progression and metastasis.
- Understanding EMT dynamics is key to developing targeted therapies.
Purpose of the Study:
- To investigate the phenotypic state transition dynamics of Epidermal Growth Factor (EGF)-induced EMT.
- To define and analyze distinct morphological cell states during EMT.
- To model the reversibility and regulatory mechanisms of EMT.
Main Methods:
- Utilized MDA-MB-468 breast cancer cell line for EGF-induced EMT studies.
- Defined three distinct morphological cell states: cobble, spindle, and circular.
- Employed quantitative image analysis and mathematical modeling to decipher state transition trajectories.
Main Results:
- Identified reversible phenotypic state transitions during EGF-induced EMT.
- Demonstrated that transition dynamics depend on EGF dose and phosphorylated EGF receptor (EGFR) levels.
- Observed an ultrasensitive switch involving phospho-EGFR controlling transitions into/out of the circular state.
Conclusions:
- The dominant trajectory was cobble → circular → spindle → cobble, highlighting system reversibility.
- Proposed a simpler discretized energy-level model as an alternative to the quasi-potential landscape model for EMT dynamics.
- Findings offer a deeper understanding of cell plasticity and potential therapeutic targets in breast cancer.
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