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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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...
Structure of a Gene01:30

Structure of a Gene

A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...
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...
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...

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Related Experiment Video

Updated: May 15, 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

An estimation method for a cellular-state-specific gene regulatory network along tree-structured gene expression

Ryo Araki1, Shigeto Seno, Yoichi Takenaka

  • 1Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka 565-0871, Japan.

Gene
|December 26, 2012
PubMed
Summary

This study introduces a novel tree-structured approach for gene regulatory network inference, improving accuracy in understanding cellular behavior across different conditions by analyzing multiple gene expression profiles effectively.

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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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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: March 7, 2018

Area of Science:

  • Systems Biology
  • Transcriptomics
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are crucial for understanding cellular behavior under varying conditions.
  • Conventional GRN inference methods often yield low accuracy and include misleading regulatory relations.
  • Existing methods analyze gene expression profiles independently, neglecting inter-condition relationships and introducing noise.

Purpose of the Study:

  • To develop a more accurate method for estimating key gene regulatory networks.
  • To enhance the understanding of cellular behavior across diverse biological conditions.
  • To overcome limitations of conventional GRN inference techniques.

Main Methods:

  • Utilizing multiple gene expression profiles organized in a tree structure.
  • Representing the original cellular state as the root and changed states as leaves.
  • Employing a tree-structured approach to infer gene regulatory networks.

Main Results:

  • The proposed method offers a more powerful estimation of gene regulatory networks.
  • Accurate inference of networks critical for understanding cellular behavior under varying conditions.
  • Demonstrated superior performance compared to conventional methods in analyzing gene expression data.

Conclusions:

  • The proposed method is more rigorous than conventional approaches for GRN inference.
  • Assumptions regarding unimportant relations in cell differentiation were validated.
  • The method accurately infers core gene regulatory networks essential for cell type characterization.