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Atlas of Computational Cell Reprogramming

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L3 · Explicit model-based inverse intervention I D T P

DEPCs (Crespo et al. 2013)

Crespo I, Perumal TM, Jurkowski W, del Sol A

2013 · BMC systems biology

BACKGROUND: Cellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes.

Abstract

From the original paper, BMC systems biology · PubMed

BACKGROUND: Cellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes. An increasing amount of experimental results show that despite the large number of genes participating in transcriptional programs of cellular phenotypes, only few key genes, which are coined here as reprogramming determinants, are required to be directly perturbed in order to induce cellular reprogramming. However, identification of reprogramming determinants still remains a combinatorial problem, and the state-of-art methods addressing this issue rests on exhaustive experimentation or prior knowledge to narrow down the list of candidates. RESULTS: Here we present a computational method, without any preliminary selection of candidate genes, to identify reduced subsets of genes, which when perturbed can induce transitions between cellular phenotypes. The method relies on the expression profiles of two stable cellular phenotypes along with a topological analysis stability elements in the gene regulatory network that are necessary to cause this multi-stability. Since stable cellular phenotypes can be considered as attractors of gene regulatory networks, cell fate and cellular reprogramming involves transition between these attractors, and therefore current method searches for combinations of genes that are able to destabilize a specific initial attractor and stabilize the final one in response to the appropriate perturbations. CONCLUSIONS: The method presented here represents a useful framework to assist researchers in the field of cellular reprogramming to design experimental strategies with potential applications in the regenerative medicine and disease modelling.

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 DEPCs (Crespo et al. 2013) 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, I, P, T
Intervention
Transcription factors
Framework
Boolean network

Software

Code
Not available
Reproducibility
Not audited
FAIR4RS

Last audited 2026-05-24

Citation

Crespo I et al. (2013). Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks., BMC systems biology.

DOI: 10.1186/1752-0509-7-140

PMID: 24350678

BibTeX
@article{depcs2013,
  title  = {Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks.},
  author = {Crespo I et al.},
  year   = {2013},
  journal = {BMC systems biology},
  pmid = {24350678},
  doi  = {10.1186/1752-0509-7-140}
}