Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
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...
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...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The rise of astrocytes: are they guardians or troublemakers of the brain disorder?

Experimental & molecular medicine·2026
Same author

Identifying Risk Factors for Dental Neglect in Children Who Failed to Complete Their Dental Surgery Appointments in Northeast Ohio: A Retrospective Study.

Children (Basel, Switzerland)·2025
Same author

Reversal of inflammatory reprogramming by vasodilator agents in pulmonary hypertension.

ERJ open research·2025
Same author

Astrocytic autophagy plasticity modulates Aβ clearance and cognitive function in Alzheimer's disease.

Molecular neurodegeneration·2024
Same author

Allele-specific control of rodent and human lncRNA KMT2E-AS1 promotes hypoxic endothelial pathology in pulmonary hypertension.

Science translational medicine·2024
Same author

Reply to Kentaro Inamura's Letter to the Editor re: Mingxiao Feng, Andres Matoso, Gabriel Epstein, et al. Identification of Lineage-specific Transcriptional Factor-defined Molecular Subtypes in Small Cell Bladder Cancer. Eur Urol. In press. https://doi.org/10.1016/j.eururo.2023.05.023.

European urology·2023

Related Experiment Video

Updated: Jun 24, 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

Modeling of gene regulatory network dynamics using threshold logic.

Tejaswi Gowda1, Sarma Vrudhula, Seungchan Kim

  • 1School of Computing and Informatics, Arizona State University, Tempe, Arizona, USA. tejaswi@asu.edu

Annals of the New York Academy of Sciences
|April 8, 2009
PubMed
Summary

This study introduces a novel gene regulatory model using threshold logic and perceptron learning. The model effectively describes and predicts biological system behaviors, replicating both steady state and transient gene dynamics.

More Related Videos

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Jun 24, 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

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Gene regulation modeling is crucial for understanding complex biological systems.
  • Existing models often focus on pairwise interactions, limiting predictive power for system-level phenomena.

Purpose of the Study:

  • To develop a novel gene regulatory model using threshold logic and perceptron learning.
  • To create a model capable of describing and predicting biological system behaviors, rather than just pairwise interactions.

Main Methods:

  • The proposed model integrates properties of threshold logic with perceptron learning techniques.
  • The approach focuses on system-level dynamics rather than detailed gene-gene interaction mapping.

Main Results:

  • The novel threshold logic-based model was applied to a 50-gene cellular system.
  • The model successfully replicated both steady-state and transient behaviors of the gene network.

Conclusions:

  • The developed model offers a robust framework for understanding and predicting gene regulatory dynamics in biological systems.
  • This threshold logic approach provides an effective method for modeling complex cellular behaviors.