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

Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
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...
Cellular Differentiation00:57

Cellular Differentiation

How does a complex organism such as a human develop from a single cell? It all starts from a single fertilized egg which gives rise to a vast array of cell types, such as nerve cells, muscle cells, and epithelial cells that characterize the adult? Throughout development and adulthood, cellular differentiation leads cells to assume their final morphology and physiology. Differentiation is the process by which unspecialized cells become specialized to carry out distinct functions.
A zygote is a...
What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...

You might also read

Related Articles

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

Sort by
Same author

Wnt-dependent spatiotemporal reprogramming of bone marrow niches drives fibrosis.

HemaSphere·2026
Same author

An oncogenic KRAS-driven secretome involving TNFα promotes niche preparation prior to pancreatic cancer onset.

Molecular cancer·2026
Same author

Modelling EWS::FLI1 protein fluctuations reveal determinants of tumor plasticity in Ewing sarcoma.

EMBO molecular medicine·2026
Same author

PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.

Nature methods·2025
Same author

PILOT-GM-VAE: patient-level analysis of single-cell disease atlas with optimal transport of Gaussian mixture variational autoencoders.

Briefings in bioinformatics·2025
Same author

Inhibiting the alarmin-driven hematopoiesis-stromal cell crosstalk in primary myelofibrosis ameliorates bone marrow fibrosis.

HemaSphere·2025
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
07:29

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Inferring differentiation pathways from gene expression.

Ivan G Costa1, Stefan Roepcke, Christoph Hafemeister

  • 1Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. filho@molgen.mpg.de

Bioinformatics (Oxford, England)
|July 1, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational model to map cell differentiation pathways using gene expression data. The method effectively reveals developmental trajectories and identifies functional gene modules in biological systems.

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

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Related Experiment Videos

Last Updated: Jul 4, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
07:29

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

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

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Area of Science:

  • Developmental Biology
  • Computational Biology
  • Systems Biology

Background:

  • Cellular differentiation and proliferation are fundamental to development.
  • Gene expression patterns across developmental stages offer insights into molecular mechanisms.
  • Previous work represented developmental gene modules using mixture models of trees.

Purpose of the Study:

  • To develop a novel computational model for analyzing gene expression profiles.
  • To create an unsupervised learning method for inferring developmental similarity and differentiation pathways.
  • To identify functional gene modules associated with specific differentiation processes.

Main Methods:

  • Proposed a novel model for gene expression profiles.
  • Developed an unsupervised learning algorithm to estimate developmental similarity.
  • Applied the method to simulated data and real gene expression data from lymphoid development.

Main Results:

  • The proposed model and method demonstrated favorable performance on simulated data compared to existing approaches.
  • Successfully inferred differentiation pathways from gene expression data.
  • Identified potentially relevant functional gene modules within the analyzed datasets.

Conclusions:

  • Incorporating structural knowledge of gene dependencies aids in revealing differentiation pathways.
  • The method is applicable to various areas of developmental biology with distinguishable cell differentiation stages.
  • This approach enhances the understanding of molecular processes underlying development using microarray data.