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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...

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Related Experiment Video

Updated: Jul 2, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Forward-backward gene expression binarization for boolean state inference over a known regulatory network.

Ismail Belgacem1, Franck Delaplace1

  • 1Independent Researcher, Mezaourou, Ghazaouet, Tlemcen, Algeria; IBISC, Univ Evry, Université Paris-Saclay, 91025 Evry, France.

Bio Systems
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

Bi4Back is a new algorithm for gene expression binarization, essential for building Boolean gene regulatory network (GRN) models. It accurately infers gene states from limited data, outperforming traditional methods.

Keywords:
BinarizationBoolean networksGene expressionGene regulatory networksODE simulations

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Last Updated: Jul 2, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Binarization of gene expression data is crucial for constructing Boolean gene regulatory network (GRN) models.
  • Existing thresholding methods are limited, oversimplifying biology and failing with sparse or single-snapshot data.

Purpose of the Study:

  • To introduce Bi4Back, a novel regulation-based binarization algorithm designed to overcome limitations of traditional methods.
  • To infer gene binary states using a known signed regulatory graph and iterative Boolean propagation.

Main Methods:

  • Bi4Back combines thresholding with iterative forward and backward Boolean propagation.
  • It utilizes a known signed regulatory graph as input and includes a dedicated inconsistency detection step.
  • The algorithm can operate on minimal data, including single steady-state measurements.

Main Results:

  • Bi4Back demonstrated exact agreement with ground truth on stable artificial networks and near-exact agreement on models converging to equilibrium.
  • Performance showed graceful degradation under oscillatory dynamics and robustness to ±50% multiplicative noise.
  • Missing data impact is linked to gene topological centrality, highlighting the importance of measuring hub genes.

Conclusions:

  • Bi4Back accurately infers gene binary states from limited and noisy omics data, enabling robust Boolean GRN model synthesis.
  • The algorithm provides a biologically consistent approach, outperforming standard thresholding methods.
  • Findings offer practical guidance for experimental design, emphasizing the measurement of central genes.