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

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,...
IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and produces two-second...
cAMP-dependent Protein Kinase Pathways01:25

cAMP-dependent Protein Kinase Pathways

Cyclic Adenosine Monophosphate (cAMP) is an essential second messenger that activates protein kinase A (PKA) and regulates various biological processes. A single epinephrine molecule binds to GPCR and activates several heterotrimeric G proteins, each stimulating multiple adenylyl cyclase, amplifying the signal, and synthesizing large numbers of cAMP molecules. Small changes in cAMP concentration affect PKA activity. The binding of four cAMP molecules induces a conformational change in PKA,...
PI3K/mTOR/AKT Signaling Pathway01:22

PI3K/mTOR/AKT Signaling Pathway

The mammalian target of rapamycin  (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1  (mTORC1) and mTOR complex 2  (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast,  mTORC2 consists of a rapamycin-insensitive companion...

You might also read

Related Articles

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

Sort by
Same author

Breast cancer genomic subtype-specific copy number alterations in metastases.

Scientific reports·2026
Same author

A scalable deep-learning framework for cancer detection using cell-free DNA shallow whole-genome sequencing.

Science advances·2026
Same author

Single-cell epigenetic landscape, microenvironment interactions, and gene regulatory modules of non-functioning pituitary adenomas.

Cell systems·2026
Same author

Pan-cancer evolution signatures link clonal expansion to dynamic changes in the tumor immune microenvironment.

Cell reports·2026
Same author

Shared multicellular injury programs of acute and chronic kidney disease enable mechanistic patient stratification.

medRxiv : the preprint server for health sciences·2026
Same author

Single-cell profiling reveals epithelial and immune responses in BK polyomavirus-infected human kidney biopsies.

JCI insight·2026

Related Experiment Video

Updated: Jul 14, 2026

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

Computational identification of cellular networks and pathways.

Florian Markowetz1, Olga G Troyanskaya

  • 1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

Molecular Biosystems
|June 21, 2007
PubMed
Summary

This study explores computational functional genomics and statistical methods to identify gene and protein networks from genomic data. It focuses on analyzing diverse data sources and establishing standards for evaluating these methods.

Related Experiment Videos

Last Updated: Jul 14, 2026

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

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Identifying functional relationships between genes and proteins is crucial for understanding biological systems.
  • Computational approaches are increasingly important for analyzing large-scale genomic datasets.

Purpose of the Study:

  • To highlight recent advancements in computational functional genomics for network identification.
  • To focus on statistical methods for discovering genetic networks.
  • To discuss the evaluation of these computational methods.

Main Methods:

  • Integrated analysis of microarray datasets.
  • Methods for combining heterogeneous genomic data sources.
  • Analysis of high-dimensional phenotyping screens.

Main Results:

  • Recent developments in computational functional genomics enable the identification of gene and protein networks.
  • Statistical methods are key for identifying genetic networks from diverse data.
  • Efforts are underway to create a gold standard for method evaluation.

Conclusions:

  • Computational functional genomics provides powerful tools for network discovery.
  • Standardized evaluation is essential for reliable identification of genetic networks.
  • Integrating diverse data sources enhances the accuracy of network identification.