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Atlas of Computational Cell Reprogramming

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L3 · Explicit model-based inverse intervention D

Toggle Switches (Okawa et al.)

Okawa S, Nicklas S, Zickenrott S, Schwamborn JC, Del Sol A

2016 · Stem cell reports

Identification of cell-fate determinants for directing stem cell differentiation remains a challenge.

Abstract

From the original paper, Stem cell reports · PubMed

Identification of cell-fate determinants for directing stem cell differentiation remains a challenge. Moreover, little is known about how cell-fate determinants are regulated in functionally important subnetworks in large gene-regulatory networks (i.e., GRN motifs). Here we propose a model of stem cell differentiation in which cell-fate determinants work synergistically to determine different cellular identities, and reside in a class of GRN motifs known as feedback loops. Based on this model, we develop a computational method that can systematically predict cell-fate determinants and their GRN motifs. The method was able to recapitulate experimentally validated cell-fate determinants, and validation of two predicted cell-fate determinants confirmed that overexpression of ESR1 and RUNX2 in mouse neural stem cells induces neuronal and astrocyte differentiation, respectively. Thus, the presented GRN-based model of stem cell differentiation and computational method can guide differentiation experiments in stem cell research and regenerative medicine.

Summary

Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how Toggle Switches (Okawa et al.) relates to the cross-modality inverse-design framework of the review.

Why this level

Level 3 because candidate interventions enter an explicit forward operator FθuF_{\theta_u} and the predicted post-intervention outcome is what scores each candidate. Representation family is executable intervention model. Cited in §3.4 of the review. Editorial rationale pending review by the maintainer.

Classification

Level
L3
Representation
Executable intervention model
Modalities
D
Intervention
Transcription factors
Framework
Boolean network

Software

Code
Not available
Reproducibility
Not audited
FAIR4RS

Last audited 2026-05-24

Citation

Okawa S et al. (2016). A Generalized Gene-Regulatory Network Model of Stem Cell Differentiation for Predicting Lineage Specifiers., Stem cell reports.

DOI: 10.1016/j.stemcr.2016.07.014

PMID: 27546532

BibTeX
@article{toggle-switches2016,
  title  = {A Generalized Gene-Regulatory Network Model of Stem Cell Differentiation for Predicting Lineage Specifiers.},
  author = {Okawa S et al.},
  year   = {2016},
  journal = {Stem cell reports},
  pmid = {27546532},
  doi  = {10.1016/j.stemcr.2016.07.014}
}