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Related Concept Videos

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...
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...
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...
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...
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

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...
What is Gene Expression?01:42

What is Gene Expression?

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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Related Experiment Video

Updated: Jun 10, 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

Modelling nonstationary gene regulatory processes.

Marco Grzegorcyzk1, Dirk Husmeier, Jörg Rahnenführer

  • 1Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany.

Advances in Bioinformatics
|August 20, 2010
PubMed
Summary
This summary is machine-generated.

Changepoint process models outperform free allocation models for inferring nonstationary gene regulatory networks from gene expression time series. This study compares both dynamic Bayesian network approaches in systems biology applications.

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An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Inferring gene regulatory networks is crucial in systems biology.
  • Dynamic Bayesian networks (DBNs) are common tools for this task.
  • Standard DBNs use linear, homogeneous models, limiting their applicability.

Purpose of the Study:

  • To compare the performance of changepoint process models and free allocation mixture models for inferring gene regulatory networks.
  • To identify theoretical shortcomings of the free allocation model.
  • To evaluate these models on simulated and real biological data.

Main Methods:

  • Utilized dynamic Bayesian networks (DBNs).
  • Compared a changepoint process-based DBN with a free allocation mixture model.
  • Applied models to simulated gene expression time series data.
  • Validated models on biological systems: viral infection in macrophages, Arabidopsis thaliana circadian regulation, and Drosophila melanogaster morphogenesis.

Main Results:

  • The changepoint process model demonstrated systematically superior performance compared to the free allocation model.
  • The changepoint model proved more effective for inferring nonstationary gene regulatory processes.
  • Both models were cross-compared on three distinct biological systems, highlighting the robustness of the changepoint approach.

Conclusions:

  • Changepoint process models offer a more robust and accurate approach for inferring nonstationary gene regulatory networks.
  • The free allocation model exhibits theoretical limitations that impact its performance.
  • This comparative analysis provides valuable insights for selecting appropriate DBN models in systems biology research.