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Related Concept Videos

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...
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
Co-activators and Co-repressors02:04

Co-activators and Co-repressors

Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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...
Transcription Factors02:16

Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...

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Related Experiment Video

Updated: Jun 15, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Hidden variable analysis of transcription factor cooperativity from microarray time courses.

D Cromer1, G K Christophides, J Stark

  • 1Imperial College London, Department of Mathematics, London, UK. d.cromer05@imperial.ac.uk

IET Systems Biology
|March 18, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to analyze gene expression data, revealing hidden transcription factor activity. The technique effectively identifies gene targets and cooperative regulation, even with measurement errors.

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Last Updated: Jun 15, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Published on: March 7, 2018

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
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Published on: June 15, 2016

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Published on: July 29, 2022

Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Gene expression is controlled by transcription factors, but their activity is hard to measure directly.
  • Existing methods identify targets of single transcription factors but struggle with complex, multi-factor regulation.

Purpose of the Study:

  • To develop a model-based analysis technique for extracting multiple 'hidden' transcription factor profiles from microarray data.
  • To predict individual transcription factor targets and quantify cooperative gene regulation.

Main Methods:

  • Utilized microarray data from wild-type and gene knock-down samples.
  • Developed an algorithm to extract two separate transcription factor activity profiles.
  • Employed simulated data for method evaluation and validation.

Main Results:

  • The method accurately classifies genes based on their regulation by individual transcription factors.
  • It effectively measures the impact of gene knock-down, even with added measurement error.
  • The algorithm quantifies cooperative regulation resulting from transcription factor interactions.

Conclusions:

  • This novel technique enhances the analysis of gene expression regulation by multiple transcription factors.
  • It provides a robust tool for understanding complex gene regulatory networks.
  • The method is effective in dissecting the roles of individual and interacting transcription factors.