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Atlas of Computational Cell Reprogramming

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L3 · Explicit model-based inverse intervention I D

IRENE

Jung S, Appleton E, Ali M, Church GM, Del Sol A

2021 · Nature communications

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies.

Abstract

From the original paper, Nature communications · PubMed

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies. However, while transcription factor over-expression-based methods have shown great promise in generating cell types in vitro, they often endure low conversion efficiency. In this context, great effort has been devoted to increasing the efficiency of current protocols and the development of computational approaches can be of great help in this endeavor. Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.

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 IRENE 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
Intervention
Transcription factors
Framework
Boolean network

Software

Reproducibility
4 of 4
FAIR4RS
0 of 5

Last audited 2026-05-24

Citation

Jung S et al. (2021). A computer-guided design tool to increase the efficiency of cellular conversions., Nature communications.

DOI: 10.1038/s41467-021-21801-4

PMID: 33712564

BibTeX
@article{irene2021,
  title  = {A computer-guided design tool to increase the efficiency of cellular conversions.},
  author = {Jung S et al.},
  year   = {2021},
  journal = {Nature communications},
  pmid = {33712564},
  doi  = {10.1038/s41467-021-21801-4}
}