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Related Concept Videos

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 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...
Operon Model01:23

Operon Model

The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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...
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...
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: Jun 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

An information theoretic approach to constructing robust Boolean gene regulatory networks.

Bane Vasić1, Vida Ravanmehr, Anantha Raman Krishnan

  • 1University of Arizona, Tucson.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|April 6, 2011
PubMed
Summary

We developed a robust 7-gene network model for cell cycle gene regulation. This model corrects gene expression errors and enables artificial cell cycles with more phases than natural ones.

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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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Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
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Area of Science:

  • Systems biology
  • Computational biology
  • Synthetic biology

Background:

  • Gene regulatory networks (GRNs) govern cellular processes, including the cell cycle.
  • Boolean network models offer a simplified framework for studying GRNs.
  • Existing models may lack robustness and error-correction capabilities.

Purpose of the Study:

  • To introduce a novel finite systems model for gene regulatory networks that exhibit cell cycle behavior.
  • To design a robust network capable of error correction in gene expression.
  • To explore the construction of artificial cell cycles with enhanced complexity.

Main Methods:

  • Extension of the Boolean network model to a finite systems model.
  • Development of a 7-gene network inspired by Projective Geometry codes.
  • Analysis of network topology, Boolean functions, and attractor structure.

Main Results:

  • The model exhibits spontaneous cycling through internal states, tracking external factors like cell mass.
  • The 7-gene network demonstrates error correction for gene expression noise.
  • The network topology is highly symmetric, utilizing simple Boolean functions.
  • The model possesses a single cycle attractor, representing high robustness.
  • This is the smallest nontrivial network with such error-correction capabilities.

Conclusions:

  • The proposed finite systems model provides a robust framework for simulating cell cycle dynamics.
  • The 7-gene Projective Geometry code-based network offers significant error correction.
  • This methodology facilitates the creation of artificial cell cycles exceeding natural complexity.
  • The findings have implications for synthetic biology and understanding cellular robustness.