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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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...

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Related Experiment Video

Updated: May 20, 2026

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

Efficient reverse-engineering of a developmental gene regulatory network.

Anton Crombach1, Karl R Wotton, Damjan Cicin-Sain

  • 1EMBL/CRG Research Unit in Systems Biology, Centre for Genomic Regulation-CRG and Universitat Pompeu Fabra-UPF, Barcelona, Spain.

Plos Computational Biology
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

Reverse engineering of gene regulatory networks aids understanding of multicellular organism development. Minimal data on expression domain boundaries, not precise levels, is sufficient for accurate network inference.

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In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression
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Last Updated: May 20, 2026

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

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Published on: June 21, 2016

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

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In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression
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In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

Published on: March 29, 2019

Area of Science:

  • Developmental biology
  • Systems biology
  • Computational biology

Background:

  • Understanding complex gene regulatory networks is crucial for deciphering multicellular organism development and evolution.
  • Computational models, specifically reverse engineering, are powerful tools for analyzing gene expression data to infer network structures and dynamics.
  • Reconstructing the spatial context and non-linear dynamics of developmental gene networks remains a significant challenge.

Purpose of the Study:

  • To address the challenge of reverse-engineering spatial developmental gene networks.
  • To investigate the minimal data requirements for accurate inference of the Drosophila melanogaster gap gene network.
  • To develop a simplified data processing pipeline to increase throughput for gene network inference.

Main Methods:

  • Utilized a case study of the gap gene network in Drosophila melanogaster for segment determination.
  • Developed a simplified data processing pipeline to increase throughput, albeit with reduced data accuracy.
  • Analyzed the impact of data accuracy on network structure inference, focusing on expression domain boundaries and levels.

Main Results:

  • Successfully inferred the correct network structure using a reduced, less accurate dataset.
  • Identified timing and position of expression domain boundaries as critical for determining regulatory network structure.
  • Demonstrated that precise measurement of expression levels is less important than boundary data for network inference.

Conclusions:

  • Established minimal data requirements for gap gene network inference, significantly reducing experimental effort.
  • Showcased the feasibility of reverse-engineering complex gene regulatory networks with less experimental data.
  • Highlighted the potential for widespread application of data-driven models in diverse developmental contexts and organisms to uncover fundamental rules of development and evolution.