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Related Concept Videos

Transcription Factors02:16

Transcription Factors

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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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Transcription Elongation Factors02:35

Transcription Elongation Factors

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Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
The transcription elongation is regulated via pausing of RNA polymerase on several occasions during transcription. In bacteria, these halts are necessary because the transcription of DNA into mRNA is coupled to the translation of that mRNA...
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Transcription Elongation Factors02:35

Transcription Elongation Factors

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No description available
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General Transcription Factors01:30

General Transcription Factors

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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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Factors Affecting Activity Coefficient01:17

Factors Affecting Activity Coefficient

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The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a...
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Factors Affecting Solubility04:01

Factors Affecting Solubility

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Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
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Related Experiment Video

Updated: Jan 24, 2026

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

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Using temporal correlation in factor analysis for reconstructing transcription factor activities.

Iosifina Pournara1, Lorenz Wernisch

  • 1School of Crystallography, Birkbeck College, University of London, Malet Street, London, UK. i.pournara@cryst.bbk.ac.uk

EURASIP Journal on Bioinformatics & Systems Biology
|July 8, 2008
PubMed
Summary
This summary is machine-generated.

This study enhances gene regulatory network analysis by incorporating time correlations into factor analysis models for transcription factor (TF) activities. This improves the reconstruction of gene regulatory networks and reveals biological periodicities in expression data.

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

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Gene regulatory networks (GRNs) model gene expression control by transcription factors (TFs).
  • Existing methods like Factor Analysis (FA) for GRN reconstruction often assume independent samples, ignoring temporal dependencies in TF activity.
  • This limitation hinders accurate prediction of TF activities and network structures, especially in time-series data.

Purpose of the Study:

  • To develop an extended Factor Analysis (FA) model that accounts for time correlations in transcription factor (TF) activity profiles.
  • To improve the reconstruction accuracy of gene regulatory networks (GRNs) by integrating temporal dynamics.
  • To capture the inherent sparsity in GRNs and assess the impact of experimental time intervals.

Main Methods:

  • Extension of traditional Factor Analysis (FA) algorithms to incorporate time-correlated transcription factor (TF) activity profiles.
  • Application of sparse connectivity matrices to better represent the biological sparsity in gene regulatory networks (GRNs).
  • Utilizing high-throughput gene expression data (e.g., microarrays) for model training and validation.

Main Results:

  • The proposed method yields significantly smoother TF activity profiles compared to previous FA algorithms.
  • Periodicities present in yeast expression data become more prominent and accurately reconstructed.
  • The estimated strength of temporal correlation provides a metric for evaluating experimental time sampling suitability.

Conclusions:

  • Incorporating time correlations into FA models significantly enhances the reconstruction of gene regulatory networks (GRNs) and TF activity prediction.
  • The developed approach offers a more biologically realistic model by accounting for temporal dependencies and network sparsity.
  • This method provides valuable insights into dynamic gene regulation and aids in optimizing experimental design for time-series studies.