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Related Experiment Video

Updated: Sep 2, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions.

Emil Andersson1, Mattias Sjö1, Keisuke Kaji2

  • 1Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Sölvegatan 14A, 221 00 Lund, Sweden.

Iscience
|August 9, 2022
PubMed
Summary

We developed CELLoGeNe, a computational tool mapping gene regulatory networks into energy landscapes. This method visualizes cell fate dynamics, identifying reprogramming roadblocks and offering insights into intracellular processes.

Keywords:
BioinformaticsCell biologyMathematical biosciencesStem cells researchSystems biology

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Area of Science:

  • Computational biology
  • Systems biology
  • Gene regulatory networks

Background:

  • Cell fate decisions are crucial in development and reprogramming.
  • Energy landscape modeling is a powerful computational approach to study cell commitment.
  • Existing methods may have limitations in analyzing gene regulatory network dynamics.

Purpose of the Study:

  • To develop a novel computational framework, CELLoGeNe, for mapping gene regulatory networks (GRNs) into energy landscapes.
  • To provide tools for visualizing and analyzing these multi-dimensional energy landscapes.
  • To apply CELLoGeNe to understand induced pluripotent stem cells (iPSCs) dynamics.

Main Methods:

  • Mapping Boolean implementations of GRNs into energy landscapes.
  • Removing symmetries in energy landscapes arising from standard Boolean operators.
  • Utilizing visualization and stochastic analysis tools for multi-dimensional landscapes.

Main Results:

  • CELLoGeNe successfully maps GRNs to energy landscapes, removing unwanted symmetries.
  • The framework visualizes epigenetic landscapes relevant to development and reprogramming.
  • Analysis of iPSC GRNs identified validated attractors and potential reprogramming roadblocks.

Conclusions:

  • CELLoGeNe offers a novel computational approach to study intracellular dynamics.
  • The framework provides a broad picture of cell fate decisions.
  • CELLoGeNe is applicable to diverse biological systems for understanding complex dynamics.