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Related Concept Videos

General Transcription Factors01:30

General 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...
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...
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...
Master Transcription Regulators02:23

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...

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Related Experiment Video

Updated: Jun 15, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Generalisation of a procedure for computing transcription factor profiles.

Z Huang1, Y Chu, B Cunha

  • 1Texas A&M University, Artie McFerrin Department of Chemical Engineering, College Station, USA.

IET Systems Biology
|March 18, 2010
PubMed
Summary

This study presents a new method to calculate quantitative transcription factor profiles from microscopy data. This advances systems biology by enabling more accurate analysis of biological systems using quantitative data.

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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
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Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

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Related Experiment Videos

Last Updated: Jun 15, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

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

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome
07:23

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Published on: June 15, 2016

Area of Science:

  • Systems biology
  • Molecular biology
  • Biophysics

Background:

  • Quantitative experimental data is crucial for systems biology approaches like parameter estimation and simulation.
  • Limited quantitative data hinders the application of these methods in life sciences.
  • Current techniques are restricted when only qualitative or semi-quantitative information is available.

Purpose of the Study:

  • To introduce a novel procedure for computing quantitative transcription factor profiles from fluorescent microscopy data.
  • To generalize existing methods for monitoring transcription factor dynamics, specifically relaxing the damped oscillation assumption.
  • To enhance the utility of systems approaches in biology by generating more quantitative data.

Main Methods:

  • Utilizes fluorescent microscopy data from green fluorescent protein (GFP) reporter cells.
  • Generalizes a previously established method for monitoring nuclear factor-kappa B (NF-κB) profiles.
  • Investigates various potential transcription factor profiles and solves the inverse problem for the gene expression model.
  • Selects the transcription factor profile that best fits the measured fluorescent intensity data.

Main Results:

  • Successfully computes quantitative transcription factor profiles from microscopy data.
  • Demonstrates the technique's applicability beyond the previously studied NF-κB damped oscillation profile.
  • Validated the method using both simulated and experimentally derived fluorescent intensity data in two case studies.

Conclusions:

  • The developed procedure effectively generates quantitative transcription factor profiles, expanding the scope of systems biology.
  • This method offers a more flexible approach to modeling transcription factor dynamics compared to previous methods.
  • The findings facilitate a deeper understanding and quantitative analysis of biological regulatory networks.