Emergent dynamics of cellular decision making in multi-node mutually repressive regulatory networks
Harshavardhan Bv1, Hanuma Sai Billakurthi2, Sarah Adigwe3
1IISc Mathematics Initiative, Indian Institute of Science, Bengaluru, Karnataka, India.
Journal of the Royal Society, Interface
|August 19, 2025
Summary
Gene regulatory networks (GRNs) control cell differentiation. This study reveals how toggle-n networks with multiple transcription factors (TFs) enable multi-lineage differentiation, generating hybrid cell phenotypes.
Area of Science:
- Developmental Biology
- Systems Biology
- Computational Biology
Background:
- Stem cell differentiation is orchestrated by gene regulatory networks (GRNs).
- Existing models primarily explain binary cell fate decisions.
- Understanding GRNs for multiple cell fates (n-terminal phenotypes) is limited.
Purpose of the Study:
- To investigate toggle-n networks for insights into multi-lineage differentiation dynamics.
- To generalize understanding of GRNs governing differentiation into multiple phenotypes.
- To explore mechanisms driving directed differentiation from hybrid phenotypes.
Main Methods:
- Analysis of toggle-n network dynamics.
- Numerical simulations of gene regulatory network models.
- Mathematical derivations and analytical solutions.
- Case study using T-helper cell differentiation.
Main Results:
- Steady-state distributions reveal co-expression of multiple cell state-specific transcription factors (TFs).
- Identification of multi-potent hybrid phenotypes during multi-lineage differentiation.
- Demonstration that cytokine signaling and asymmetric regulatory links direct hybrid cell differentiation.
Conclusions:
- Toggle-n networks provide a framework for understanding multi-lineage differentiation.
- Hybrid cell phenotypes are a key intermediate state in complex differentiation processes.
- Asymmetric regulatory links and signaling pathways are crucial for directed cell fate decisions.
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