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Related Concept Videos

Constitutive and Regulated Gene Expression01:27

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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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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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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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Related Experiment Video

Updated: Apr 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A framework for modelling gene regulation which accommodates non-equilibrium mechanisms.

Tobias Ahsendorf1,2, Felix Wong3,4, Roland Eils5,6

  • 1DKFZ, Heidelberg, D-69120, Germany. tobias.ahsendorf@googlemail.com.

BMC Biology
|December 6, 2014
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Summary

This study introduces a novel graph-based framework to analyze gene regulation beyond thermodynamic equilibrium, revealing complex, history-dependent mechanisms in eukaryotic gene expression. This approach accommodates epigenetic factors and offers new insights into gene function.

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Area of Science:

  • Systems biology
  • Computational biology
  • Epigenetics

Background:

  • Traditional gene regulation analysis assumes thermodynamic equilibrium, limiting understanding of epigenetic mechanisms.
  • Existing models struggle to incorporate energy-dissipating processes like DNA methylation and histone modifications.
  • Eukaryotic gene expression involves complex interactions between transcription factors and epigenetic regulators.

Purpose of the Study:

  • To develop a new framework for analyzing gene regulation that includes non-equilibrium mechanisms.
  • To model gene regulatory systems using graph theory and stochastic master equations.
  • To explore the implications of non-equilibrium dynamics on gene expression complexity.

Main Methods:

  • Introduced a graph-based framework representing DNA microstates, transitions, and rates.
  • Derived a stochastic master equation to describe microstate probability changes over time.
  • Applied the framework to models of steroid-hormone responsive genes, chromatin domains, and the yeast PHO5 gene.

Main Results:

  • The graph framework successfully accommodates non-equilibrium mechanisms in gene regulation.
  • Analysis of PHO5 gene regulation revealed unexpected complexity arising from non-equilibrium conditions.
  • Demonstrated that non-equilibrium systems exhibit history-dependent behavior, unlike equilibrium systems.
  • Introduced a graph-based concept of independence to manage complexity in non-equilibrium sub-systems.

Conclusions:

  • The graph-based framework provides a broader foundation for understanding gene function, especially with increasing epigenomic data.
  • Gene function may increasingly be represented by graphs, analogous to sequence-based gene structure representation.
  • The developed methods are crucial for interpreting complex, history-dependent gene regulation in eukaryotes.