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Toward Modeling Context-Specific EMT Regulatory Networks Using Temporal Single Cell RNA-Seq Data.

Daniel Ramirez1, Vivek Kohar2, Mingyang Lu2

  • 1College of Health Solutions, Arizona State University, Tempe, AZ, United States.

Frontiers in Molecular Biosciences
|May 12, 2020
PubMed
Summary
This summary is machine-generated.

This study models context-specific gene regulatory circuits for epithelial-mesenchymal transition (EMT) using bioinformatics and mathematical modeling. The approach reveals distinct regulatory dynamics and identifies factors influencing model quality for cancer research.

Keywords:
cancerepithelial-mesenchymal transitionnetwork modelingphenotypic plasticitysingle-cell RNA-seq

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Cancer Research

Background:

  • Epithelial-mesenchymal transition (EMT) is critical in cancer progression and a therapeutic target.
  • Existing models of EMT gene regulatory circuits (GRCs) lack context-specific details.
  • Transcriptomics data offers a potential source for context-specific GRC construction.

Purpose of the Study:

  • To develop and assess a combined bioinformatics and mathematical modeling approach for constructing context-specific EMT GRCs.
  • To identify context-specific activity dynamics of EMT transcription factors.
  • To understand the influence of bioinformatics parameters and network structures on GRC quality.

Main Methods:

  • Utilized time-series single-cell RNA-sequencing data from four cancer cell lines under EMT-inducing conditions.
  • Employed Random Circuit Perturbation (RACIPE) for mathematical modeling to identify optimal GRCs.
  • Analyzed dynamics of key regulators like NF-KB and AP-1 transcription factors.

Main Results:

  • Identified context-specific activity dynamics for common EMT transcription factors.
  • Observed distinct regulatory paths for forward and backward EMT transitions.
  • Demonstrated that the approach's success in building high-quality GRCs varies, influenced by parameters and network properties.

Conclusions:

  • The integration of bioinformatics and systems biology modeling can effectively elucidate context-specific gene regulatory mechanisms.
  • The study highlights the importance of both computational parameters and network topology in GRC construction.
  • This approach holds promise for understanding cellular state transitions in various biological contexts.