Robustness in phenotypic plasticity and heterogeneity patterns enabled by EMT networks.

Area of Science:

• Systems Biology
• Cancer Research
• Computational Biology

Background:

• Epithelial-mesenchymal plasticity (EMP) is crucial for cancer metastasis, enabling cells to switch between epithelial, mesenchymal, and hybrid phenotypes.
• While multistable regulatory networks driving EMP are known, the principles ensuring their robustness across contexts remain unclear.

Purpose of the Study:

• To investigate the dynamic and structural robustness of EMP networks concerning phenotypic heterogeneity and plasticity.
• To identify design principles that confer robustness to EMP networks.

Main Methods:

• Simulations using a continuous state space Boolean model and an ODE-based parameter-agnostic framework (RACIPE).
• Perturbations to network topology and parameters to assess robustness.
• Development of a metric based on feedback loop characteristics to quantify robustness.

Main Results:

• Multistable EMP networks exhibit greater structural and dynamic robustness than randomized networks, with topological hallmarks identified.
• Robustness is governed by a balance of positive and negative feedback loops.
• A novel metric effectively quantifies network robustness and can predict fragilities without full simulation.

Conclusions:

• Network topology plays a critical role in the robustness of phenotypic heterogeneity and plasticity in EMP.
• The identified metric provides a powerful tool for analyzing and comparing EMP network robustness.
• Understanding these principles can guide the identification of therapeutic vulnerabilities in cancer metastasis.