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Atlas of Computational Cell Reprogramming

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L3 · Explicit model-based inverse intervention D P

BoolIFFNN

Choo SM, Almomani LM, Cho KH

2020 · Frontiers in physiology

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them.

Abstract

From the original paper, Frontiers in physiology · PubMed

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.

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 BoolIFFNN 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, P
Intervention
Transcription factors
Framework
Boolean network

Software

Code
Not available
Reproducibility
Not audited
FAIR4RS

Last audited 2026-05-24

Citation

Choo SM et al. (2020). Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion., Frontiers in physiology.

DOI: 10.3389/fphys.2020.594151

PMID: 33335489

BibTeX
@article{booliffnn2020,
  title  = {Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion.},
  author = {Choo SM et al.},
  year   = {2020},
  journal = {Frontiers in physiology},
  pmid = {33335489},
  doi  = {10.3389/fphys.2020.594151}
}