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Related Concept Videos

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...
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...
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...
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...
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...
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...

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Related Experiment Video

Updated: Jun 21, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

Comparing different ODE modelling approaches for gene regulatory networks.

A Polynikis1, S J Hogan, M di Bernardo

  • 1Department of Engineering Mathematics, University of Bristol, Queen's Building, University Walk, Bristol BS8 1TR, UK. Th.Polynikis@bristol.ac.uk

Journal of Theoretical Biology
|August 12, 2009
PubMed
Summary
This summary is machine-generated.

Mathematical models are crucial for synthetic biology and systems biology. This study compares different modeling approaches for gene regulatory networks, revealing that various models can yield conflicting conclusions on system dynamics and stability.

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Area of Science:

  • Systems Biology
  • Synthetic Biology
  • Mathematical Biology

Background:

  • Mathematical models are essential for analyzing and designing biological systems, particularly gene regulatory networks.
  • Commonly used models involve nonlinear ordinary differential equations (ODEs) with Hill functions, often simplified using quasi-steady-state assumptions for mRNA dynamics.
  • Alternative approaches include piecewise-linear approximations of Hill functions and discrete-time maps.

Purpose of the Study:

  • To discuss and compare different mathematical modeling approaches for gene regulatory networks.
  • To evaluate the impact of various modeling simplifications on the derived system dynamics.
  • To analyze how different models affect conclusions about the existence and stability of equilibria and oscillations.

Main Methods:

  • Comparison of ordinary differential equation (ODE) models with Hill functions.
  • Analysis of models employing quasi-steady-state approximations for mRNA dynamics.
  • Evaluation of piecewise-linear approximations and discrete-time map models.
  • Application to a representative gene regulatory network.

Main Results:

  • Different mathematical models and approximations can lead to conflicting conclusions regarding the stability of equilibria and oscillatory behaviors.
  • The choice of modeling framework significantly influences the predicted dynamics of gene regulatory networks.
  • The viability and effects of approximations like quasi-steady-state assumptions and piecewise-linear functions on system dynamics were discussed.

Conclusions:

  • The selection of a mathematical modeling approach is critical and can substantially alter the interpretation of gene regulatory network behavior.
  • Researchers must carefully consider the implications of chosen approximations and modeling frameworks to avoid drawing erroneous conclusions.
  • Further investigation into the trade-offs between model complexity and accuracy is warranted for robust systems and synthetic biology applications.