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Updated: Mar 30, 2026

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Constructing a holistic map of cell fate decision by hyper solution landscape.

Xiaoyi Zhang1, Zhiyuan Li2, Lei Zhang3

  • 1Center for Quantitative Biology, Peking University, Beijing 100871, China.

Cell Systems
|March 28, 2026
PubMed
Summary

The hyper solution landscape (HSL) offers a new way to study cell fate decisions by simplifying complex gene regulatory networks. This method helps design strategies for cell reprogramming by revealing how different paths affect cell fate outcomes.

Keywords:
Waddington landscapebifurcationcell fate decisiongene regulatory networkhyper solution landscapesolution landscape

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Area of Science:

  • Systems biology
  • Computational biology
  • Molecular and Cell Biology

Background:

  • The Waddington landscape metaphor is widely used for quantitative studies of cell fate decisions.
  • Dynamical systems approaches face limitations due to nonlinear complexity and parameter dependence in analyzing cell fate transitions.

Purpose of the Study:

  • Introduce the hyper solution landscape (HSL) as a minimally parameter-dependent methodology.
  • Provide a comprehensive structure of all possible gene regulatory network configurations.
  • Enable systematic analysis of cell fate transitions and rational design of transition strategies.

Main Methods:

  • Developed the hyper solution landscape (HSL) methodology.
  • Connected different solution landscapes to reflect dynamic changes associated with bifurcations.
  • Applied HSL to the cross-inhibition with self-activation motif and the seesaw model of cellular reprogramming.

Main Results:

  • HSL reveals comprehensive landscape structures for gene regulatory networks.
  • Identified key hyperparameters influencing cell-fate propensity.
  • Demonstrated that different routes through HSL yield distinct cell fate distributions.
  • Validated HSL utility in cellular reprogramming models.

Conclusions:

  • HSL provides a powerful framework for understanding and engineering cell fate decisions.
  • Minimally parameter-dependent methodology simplifies analysis of complex biological systems.
  • Enables rational design of cell fate transition strategies for applications like cellular reprogramming.