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Related Concept Videos

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Related Experiment Video

Updated: Feb 27, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A deterministic method for estimating free energy genetic network landscapes with applications to cell commitment and

Victor Olariu1,2, Erica Manesso1, Carsten Peterson1

  • 1Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund 22362, Sweden.

Royal Society Open Science
|July 7, 2017
PubMed
Summary

This study introduces a novel method to map gene regulatory networks onto free energy landscapes, enabling predictions of cell development and reprogramming. This approach bypasses complex simulations by linking rate equations to energy landscapes.

Keywords:
deterministic modelsenergy landscapereprogrammingstem cell commitment

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Area of Science:

  • Systems biology
  • Computational biology
  • Biophysics

Background:

  • Modeling cellular development and reprogramming often uses genetic landscapes, but lacks quantitative free energy functions for dynamics.
  • Existing models struggle with accurate predictions due to limitations of Michaelis-Menten or Hill rate equations.

Purpose of the Study:

  • To develop a deterministic method for estimating free energy landscapes of interacting gene systems.
  • To enable quantitative predictions of cell states and optimal paths for processes like cell reprogramming.
  • To provide an alternative to exhaustive simulations by linking rate equations to free energy.

Main Methods:

  • Developed a method to approximate free energy landscapes from sigmoidal rate equations for gene networks.
  • Estimated free energy surfaces for systems of interacting genes under varying noise levels (temperatures).
  • Adapted a shortest path algorithm to identify optimal routes within these estimated landscapes.

Main Results:

  • Successfully estimated free energy landscapes for gene interaction networks.
  • Demonstrated the method's application on circuits involved in hematopoiesis and embryonic stem cell development.
  • Identified sequential steps and external factor timings for efficient cell reprogramming scenarios.

Conclusions:

  • The developed method provides a powerful tool for analyzing cell state transitions and optimizing reprogramming strategies.
  • Linking gene network dynamics to free energy landscapes offers a more efficient approach to understanding and manipulating cellular processes.
  • This framework facilitates the prediction of optimal paths for cell fate decisions and external factor interventions.