Landscape and kinetic path quantify critical transitions in epithelial-mesenchymal transition

Area of Science:

• Cell Biology
• Systems Biology
• Cancer Research

Background:

• Epithelial-mesenchymal transition (EMT) is crucial for development and cancer metastasis.
• Current understanding of EMT's complex molecular mechanisms and early warning signals is limited.
• Quantifying EMT's global stability and stochastic dynamics requires advanced modeling.

Purpose of the Study:

• To construct a comprehensive gene regulatory network model for EMT.
• To quantify the potential landscape and transition dynamics of EMT.
• To identify a robust measure for early warning signals predicting EMT transitions.

Main Methods:

• Developed a gene regulatory network model for EMT.
• Quantified the potential landscape and identified stable attractors (E, M, intermediate states).
• Utilized path-integral approach to determine most probable EMT transition paths.
• Integrated landscape and path analysis with early warning theory concepts.

Main Results:

• The EMT potential landscape revealed multiple stable states and intermediate states.
• Identified transition paths supported by experimental data, showing intermediate states accelerate EMT.
• Proposed potential barrier height as a global and accurate early warning signal for EMT.
• Provided a quantitative explanation for the early warning theory in EMT.

Conclusions:

• The study advances mechanistic understanding of EMT dynamics and the role of intermediate states.
• Potential barrier height offers a novel measure for predicting critical transitions in EMT.
• The findings provide quantitative insights into early warning theory for complex biological systems.