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Multistability in the epithelial-mesenchymal transition network.

Ying Xin1, Bree Cummins2, Tomáš Gedeon3

  • 1Department of Ophthalmology (Wilmer Eye Institute), Johns Hopkins University School of Medicine, Baltimore, USA.

BMC Bioinformatics
|February 26, 2020
PubMed
Summary
This summary is machine-generated.

The epithelial-mesenchymal transition (EMT) network exhibits complex dynamics, with stable epithelial (E) and mesenchymal (M) states often coexisting with other stable states across various parameters. This suggests cell response to signals depends on specific genetic backgrounds.

Keywords:
Epithelial-mesenchymal transitionMultistabilityNetwork models

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Area of Science:

  • Cell biology
  • Systems biology
  • Computational biology

Background:

  • Epithelial-mesenchymal transition (EMT) is crucial for development and cancer metastasis.
  • The existence and number of intermediate states in EMT are debated.
  • Previous models explored EMT states by varying limited parameters.

Purpose of the Study:

  • To comprehensively explore intermediate states in an EMT model network.
  • To analyze the full parameter space of EMT dynamics.
  • To understand the prevalence of different phenotypic states.

Main Methods:

  • Utilized the Dynamic Signatures Generated by Regulatory Networks (DSGRN) tool.
  • Computed summaries of network dynamics across all parameter space.
  • Identified attractors and characterized steady states.

Main Results:

  • Only equilibrium attractors were found in the EMT network.
  • Epithelial (E) and mesenchymal (M) states are dominant across parameter space.
  • Bistability and multistability are common, with E or M states coexisting with others.

Conclusions:

  • Multistability is broadly present in the EMT network.
  • Cellular response to signals is highly dependent on cell line and genetic background.
  • The complexity of EMT dynamics necessitates a parameter-aware approach.