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
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.
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.
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