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Atlas of Computational Cell Reprogramming

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C3 · Explicit model based inverse intervention D T P

Class C3 records a modeled outcome or a certified control property. The claim reaches only as far as the model, its reachable set, and the outcome it predicts. Boundary conditions.

CAESAR

Kim N, Lee J, Kim J, Kim Y, Cho KH

2024 · Briefings in bioinformatics

The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that…

Abstract

From the original paper, Briefings in bioinformatics · PubMed

The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that critically determines distinct cell fates. Reprogramming and trans-differentiation clearly show examples of cell fate change by regulating only a few or even a single molecular switch. However, it is still unknown how to identify such a switch, called a master regulator, and how cell fate is determined by its regulation. Here, we present CAESAR, a computational framework that can systematically identify master regulators and unravel the resulting canalizing kernel, a key substructure of interconnected feedbacks that is critical for cell fate determination. We demonstrate that CAESAR can successfully predict reprogramming factors for de-differentiation into mouse embryonic stem cells and trans-differentiation of hematopoietic stem cells, while unveiling the underlying essential mechanism through the canalizing kernel. CAESAR provides a system-level understanding of how complex molecular networks determine cell fates.

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 CAESAR relates to the inverse-design framework of the review.

Why this class

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

Class
C3 · Explicit model based inverse intervention
Representation
Executable intervention model
Settings
D, P, T
Intervention
TFs + other molecular targets
Framework
Boolean network

Software

Reproducibility
3/4
FAIR4RS
0/5

Last audited 2026-06-01

Citation

Kim N et al. (2024). Canalizing kernel for cell fate determination., Briefings in bioinformatics.

DOI: 10.1093/bib/bbae406

PMID: 39171985

BibTeX
@article{caesar2024,
  title  = {Canalizing kernel for cell fate determination.},
  author = {Kim N et al.},
  year   = {2024},
  journal = {Briefings in bioinformatics},
  pmid = {39171985},
  doi  = {10.1093/bib/bbae406}
}