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Emergent Criticality in Coupled Boolean Networks.

Chris Kang1, Madelynn McElroy1,2, Nikolaos K Voulgarakis1

  • 1Department of Mathematics and Statistics, Washington State University, Pullman, WA 99164, USA.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Early embryonic development mimics phase transitions. Coupled Boolean networks model stem cell differentiation, revealing symmetry-breaking events driven by gene expression noise and signaling interactions.

Keywords:
cell differentiationcoupled Boolean networksmultilayer Ising modelself-tuned criticalitysymmetry breaking

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Area of Science:

  • Computational Biology
  • Developmental Biology
  • Statistical Mechanics

Background:

  • Early embryonic development transforms identical stem cells into diverse specialized cell types.
  • This differentiation process involves sequential symmetry-breaking events, transitioning from a high-symmetry stem cell state to low-symmetry specialized cells.
  • The observed developmental dynamics resemble phase transitions studied in statistical mechanics.

Purpose of the Study:

  • To theoretically investigate the hypothesis that embryonic stem cell differentiation mirrors phase transitions.
  • To model embryonic stem cell (ESC) populations and their differentiation dynamics using computational approaches.
  • To analyze how system parameters influence symmetry breaking and cell fate determination.

Main Methods:

  • Utilized a coupled Boolean network (BN) model to simulate embryonic stem cell populations.
  • Employed a multilayer Ising model to represent cell-cell interactions, including paracrine and autocrine signaling.
  • Incorporated external interventions and gene expression noise into the model parameters.

Main Results:

  • Demonstrated that cell-to-cell variability can be represented as a mixture of steady-state probability distributions.
  • Observed that the BN model undergoes first- and second-order phase transitions influenced by gene expression noise and interaction strengths.
  • Showed that these phase transitions lead to spontaneous symmetry breaking, generating distinct cell types with unique steady-state distributions.

Conclusions:

  • Embryonic stem cell differentiation can be effectively modeled as a series of phase transitions.
  • Coupled Boolean networks provide a framework for understanding spontaneous symmetry breaking and cell fate decisions.
  • The model suggests that self-organization within coupled BNs can drive spontaneous cell differentiation.