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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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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...
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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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...
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What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

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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.
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Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

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Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
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Structure of a Gene01:30

Structure of a Gene

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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...
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Related Experiment Video

Updated: Jul 28, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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CNNGRN: A Convolutional Neural Network-Based Method for Gene Regulatory Network Inference From Bulk Time-Series

Zhen Gao, Jin Tang, Junfeng Xia

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    This study introduces CNNGRN, a new method for reconstructing gene regulatory networks (GRNs) using gene expression data and network structure. CNNGRN improves prediction accuracy for biological networks.

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    Area of Science:

    • Systems Biology
    • Computational Biology
    • Genomics

    Background:

    • Gene regulatory networks (GRNs) are crucial for biological processes.
    • Current GRN reconstruction methods have limitations in predictive performance.
    • Existing methods often overlook network structure information.

    Purpose of the Study:

    • To develop a more effective method for GRN reconstruction.
    • To integrate gene expression data with network structure information.
    • To improve the accuracy of inferring gene regulatory relationships.

    Main Methods:

    • Proposed a supervised model named CNNGRN.
    • Utilized convolutional neural network (CNN) for feature extraction.
    • Integrated bulk time-series gene expression data and ground-truth GRN structure as inputs.
    • Performed feature importance visualization to identify key features.

    Main Results:

    • CNNGRN demonstrated competitive performance on benchmark datasets.
    • Outperformed existing state-of-the-art computational methods.
    • Identified key features contributing to GRN reconstruction accuracy.

    Conclusions:

    • CNNGRN offers an effective approach for GRN reconstruction.
    • The identified hub genes are validated through literature, confirming their biological relevance.
    • The method advances systems biology by providing more accurate network inference.