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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by
Same author

A ligandable PNT domain establishes ERG as a directly targetable oncogenic driver in prostate cancer.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Asynchronous evolution of epithelium and stroma differentiates precursor lesions from pancreatic cancer.

Cancer discovery·2026
Same author

Rewiring Oncogenic Transcriptional Complexes with Domain-ALTeration Chimeras (DALTACs) in Prostate Cancer.

bioRxiv : the preprint server for biology·2026
Same author

ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging.

bioRxiv : the preprint server for biology·2026
Same author

Mapping the Tumor Microenvironment with Integrative Single-Cell RNA Sequencing and Spatial Proteomics: Uncovering Mechanisms of Disease and Therapeutic Resistance.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

From Raw Data to Biological Insights: A Practical Guide for Spatial Transcriptomics Analysis in R and Python.

Methods in molecular biology (Clifton, N.J.)·2026

Related Experiment Video

Updated: Jul 7, 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

Inferring time-varying network topologies from gene expression data.

Arvind Rao1, Alfred O Hero, David J States

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2122, USA.

EURASIP Journal on Bioinformatics & Systems Biology
|March 1, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces regime-SSM, a novel method for identifying dynamic gene regulatory networks that change over time. This approach captures cellular state variations, offering a more accurate representation than static models.

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Related Experiment Videos

Last Updated: Jul 7, 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

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Current gene regulatory network (GRN) identification methods often infer static, steady-state networks.
  • This steady-state assumption is challenged by the dynamic nature of cellular processes and varying gene interactions.
  • There is a critical need for methods that can represent GRNs in a time-varying manner to reflect different cellular states.

Purpose of the Study:

  • To develop and present a novel approach, regime-SSM, for inferring dynamic gene regulatory networks.
  • To model gene interactions that vary over time, accounting for distinct cellular states.
  • To provide a more accurate representation of gene regulation compared to traditional steady-state models.

Main Methods:

  • The regime-SSM approach employs a clustering technique based on inferred network dynamics.
  • System identification using state-space models is applied to each identified cluster.
  • This process infers a time-varying network adjacency matrix representing gene interactions.

Main Results:

  • The regime-SSM method was applied to the mouse embryonic kidney dataset.
  • The approach was also validated on a T-cell activation gene expression dataset.
  • Results demonstrated conformity with existing experimental evidence, validating the dynamic network inference.

Conclusions:

  • The regime-SSM approach effectively infers dynamic gene regulatory networks.
  • This method accurately captures time-varying gene interactions influenced by cellular states.
  • The findings support the utility of dynamic network modeling in understanding complex biological systems.