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

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...
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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...

You might also read

Related Articles

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

Sort by
Same author

In vivo clonotypic regulation of human myelin basic protein-reactive T cells by T cell vaccination.

Journal of immunology (Baltimore, Md. : 1950)·1995
Same author

Superantigen reactivity of gamma delta T cell clones isolated from patients with multiple sclerosis and controls.

Cellular immunology·1995
Same author

Tissue distribution of cocaine methyl esterase and ethyl transferase activities: correlation with carboxylesterase protein.

The Journal of pharmacology and experimental therapeutics·1995
Same author

Suppression of insulitis in non-obese diabetic (NOD) mice by oral insulin administration is associated with selective expression of interleukin-4 and -10, transforming growth factor-beta, and prostaglandin-E.

The American journal of pathology·1995
Same author

Molecular cloning and characterization of NF-IL3A, a transcriptional activator of the human interleukin-3 promoter.

Molecular and cellular biology·1995
Same author

A potential vulnerability locus for schizophrenia on chromosome 6p24-22: evidence for genetic heterogeneity.

Nature genetics·1995

Related Experiment Video

Updated: Jun 18, 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

Combinatorial regulation: characteristics of dynamic correlations.

J Zhang1, Z Yuan, T Zhou

  • 1Sun Yet-Sen University, School of Mathematical and Computational Sciences, Guangzhou, People's Republic of China. mcszhtsh@mail.sysu.edu.cn

IET Systems Biology
|December 2, 2009
PubMed
Summary

Researchers analyzed gene expression logic using stochastic modeling. Dynamic cross-correlations reveal distinct curve shapes for AND and OR logic, distinguishing gene regulatory scenarios.

More Related Videos

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells
14:12

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Related Experiment Videos

Last Updated: Jun 18, 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

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells
14:12

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Cis-regulatory modules integrate multiple transcription factor inputs.
  • Gene expression is often controlled via combinatorial logic.

Purpose of the Study:

  • To analyze dynamic cross-correlations between regulator concentrations and gene output.
  • To characterize these correlations for different gene regulatory logic operations (AND, OR).
  • To assess the role of intrinsic and extrinsic noise.

Main Methods:

  • Stochastic modeling of gene regulatory networks.
  • Analysis of dynamic cross-correlation functions.
  • Investigating the impact of noise on correlation characteristics.

Main Results:

  • Dynamic cross-correlation curves exhibit distinct convexity for AND (upwards) and OR (downwards) logic near zero correlation time.
  • Intrinsic and extrinsic noise play specific roles in shaping these correlations.
  • The cross-correlation function serves as a robust index for distinguishing gene expression logic.

Conclusions:

  • The cross-correlation function is a valuable tool for differentiating between super- and sub-additive gene regulatory mechanisms.
  • This method accurately represents gene expression logic, analogous to molecular AND and OR gates.