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Related Concept Videos

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...
Global Regulatory Systems01:28

Global Regulatory Systems

Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...
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...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...

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Related Experiment Video

Updated: Jul 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

Do scale-free regulatory networks allow more expression than random ones?

Miguel A Fortuna1, Carlos J Melián

  • 1Integrative Ecology Group, Estación Biológica de Doñana, CSIC, Avda. Ma Luisa s/n, 41013, E-41080 Sevilla, Spain. fortuna@ebd.csic.es

Journal of Theoretical Biology
|April 25, 2007
PubMed
Summary

Scale-free networks, analogous to gene regulatory networks, support larger functional systems than random networks. This suggests small genomes with scale-free structures may allow greater gene expression.

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Related Experiment Videos

Last Updated: Jul 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

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression
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A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene regulatory networks control gene expression and cellular function.
  • Understanding network topology is crucial for predicting system behavior.
  • Software package dependencies in operating systems offer a model for complex networks.

Purpose of the Study:

  • To investigate the relationship between network topology and system size using software package dependencies as an analogy for gene regulatory networks.
  • To compare the functional capacity of scale-free networks versus random (exponential) networks.
  • To explore potential implications for genome size, gene expression, and evolution.

Main Methods:

  • Compilation of software package regulatory interactions (dependencies and conflicts) from Debian GNU/Linux.
  • Assembly of networks using a trace-back algorithm, generating both scale-free and exponential topologies.
  • Measurement of the maximum number of functionally installable packages (active network size).

Main Results:

  • Scale-free regulatory networks demonstrated a larger active network size compared to random (exponential) networks.
  • The findings suggest that scale-free topologies can support more functional components within a system.
  • This indicates a potential advantage for systems with scale-free organization.

Conclusions:

  • Scale-free regulatory networks may enable greater gene expression in biological systems compared to random networks.
  • Small genomes with scale-free regulatory topologies could potentially support more gene expression than larger genomes with exponential topologies.
  • These findings may have significant implications for the dynamics, robustness, and evolutionary trajectories of genomes.