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

1.3K
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...
1.3K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

20.2K
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...
20.2K
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

3.1K
3.1K
Epistasis Analysis01:09

Epistasis Analysis

4.9K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
4.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
359
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

4.7K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
4.7K

You might also read

Related Articles

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

Sort by
Same author

Long-term effects of multisession gamma transcranial alternating current stimulation in Alzheimer's disease.

Journal of Alzheimer's disease : JAD·2026
Same author

Corrigendum to "Towards a universal size distribution in a polymer network. Implications for drug delivery and plasmonic nanoparticle transport phenomena in polysaccharide and synthetic hydrogels" [Int. J. Biol. Macromol. 317 (2025), 144741].

International journal of biological macromolecules·2026
Same author

A RiboCancer cell line panel reveals that CLL-associated Rps15 mutations translationally rewire transcription through codon-specific tRNA accommodation defects.

HemaSphere·2026
Same author

Mucoactive Agents in Muco-Obstructive Lung Diseases: A Critical Reappraisal of Pharmacological Effects and Clinical Outcomes.

Pharmaceuticals (Basel, Switzerland)·2026
Same author

Pilot Study on the Use of Rheology and Low Field Nmr to Characterize the Liver of Obese Patients Undergoing Metabolic and Bariatric Surgery.

International journal of molecular sciences·2026
Same author

Cumulative Incidence in Monogenic Alzheimer's Disease and Frontotemporal Dementia: Gene-Gene Interaction Effect.

International journal of molecular sciences·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 28, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

3.8K

Investigating perturbed pathway modules from gene expression data via structural equation models.

Daniele Pepe1, Mario Grassi

  • 1Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy. danielepepe84@gmail.com.

BMC Bioinformatics
|June 3, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework using Structural Equation Modeling (SEM) to analyze gene expression data, identifying perturbed pathways and gene connections in neurological diseases like frontotemporal lobar degeneration and multiple sclerosis.

More Related Videos

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

1.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

Related Experiment Videos

Last Updated: Apr 28, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

3.8K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

1.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.0K

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Phenotypical diversity arises from complex intracellular network perturbations, not single gene dysregulation.
  • High-throughput gene expression data enable investigation of gene expression profiles across conditions.
  • Identifying perturbed biological pathways from differentially expressed genes (DEGs) is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To develop a pipeline for investigating pathway modules using Structural Equation Modeling (SEM).
  • To analyze gene expression variations and inter-gene connections in relation to different phenotypes.
  • To identify deregulated genes and their connections within perturbed pathways.

Main Methods:

  • Utilized a pipeline based on Structural Equation Modeling (SEM).
  • Applied the procedure to microarray data from frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U) and multiple sclerosis (MS) datasets.
  • Generated pathway models from DEGs and biological databases to infer causal structures, followed by SEM analysis.

Main Results:

  • Confirmed the importance of specific genes in the analyzed neurological diseases.
  • Unveiled modified connections among genes within perturbed pathways.
  • Demonstrated the ability to detect differential gene expression and differential connections between genes.

Conclusions:

  • Propose a SEM-based framework for differential gene expression analysis of microarray data.
  • The framework identifies relevant genes, perturbed pathways, and disease modules.
  • It detects differential gene expression and modifications in gene-gene relationships, offering insights into disease mechanisms.