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Related Concept Videos

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...
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...
Signal Flow Graphs01:18

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
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...

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Related Experiment Video

Updated: May 12, 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 flood-based information flow analysis and network minimization method for gene regulatory networks.

Andreas Pavlogiannis1, Vadim Mozhayskiy, Ilias Tagkopoulos

  • 1Department of Computer Science, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.

BMC Bioinformatics
|April 27, 2013
PubMed
Summary

Network flooding simplifies complex biological networks by identifying minimal sub-networks and analyzing information flow. This scalable method aids in understanding gene regulatory pathways and cellular responses.

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

Published on: August 16, 2017

Related Experiment Videos

Last Updated: May 12, 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

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

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

Published on: August 16, 2017

Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Biological networks exhibit high interconnectivity and complex topologies, complicating the identification of condition-specific sub-networks.
  • Scalable methods are needed to reveal information flow in gene regulatory and biochemical pathways for identifying key participants and paths in specific cellular contexts.

Purpose of the Study:

  • Introduce the theory of network flooding to address network minimization and regulatory information flow in gene regulatory networks.
  • Develop a scalable algorithm to find minimal sub-networks and analyze information flow from source to sink nodes.

Main Methods:

  • Developed a novel, scalable network traversal algorithm based on the theory of network flooding.
  • Applied the algorithm to synthetic and E. coli networks to assess network size reduction and information flow analysis.

Main Results:

  • The network flooding method effectively identifies minimal sub-networks encoding regulatory programs.
  • The algorithm demonstrates scalability and robustness to noise and missing data in biological networks.
  • Significant network size reduction was achieved in both synthetic and E. coli networks.

Conclusions:

  • Network flooding offers a practical approach for information flow analysis in gene regulatory networks.
  • The theory has potential for a unifying framework for simultaneous network minimization and information flow analysis across multi-omics levels.