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

Transcription Factors02:16

Transcription Factors

76.4K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
76.4K
General Transcription Factors01:30

General Transcription Factors

5.5K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
5.5K
Master Transcription Regulators02:23

Master Transcription Regulators

7.0K
Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
7.0K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.4K
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...
8.4K
Forced Transdifferentiation01:28

Forced Transdifferentiation

2.0K
Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial...
2.0K
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

9.4K
Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
9.4K

You might also read

Related Articles

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

Sort by
Same author

Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets.

Nature cancer·2026
Same author

Sequential transcriptional waves and NF-κB-driven chromatin remodeling direct drug-induced dedifferentiation in cancer.

Nature communications·2026
Same author

Robust predictors for drug response of patients with acute myeloid leukemia.

PloS one·2026
Same author

KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs.

bioRxiv : the preprint server for biology·2026
Same author

Drivers of clinical resistance to venetoclax and hypomethylating agents in acute myeloid leukemia and strategies for improving efficacy.

HemaSphere·2026
Same author

Cellular heterogeneity and therapeutic response profiling of human IDH + glioma stem cell cultures.

Scientific reports·2025

Related Experiment Video

Updated: Aug 29, 2025

Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters
09:22

Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters

Published on: November 26, 2013

14.6K

Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation.

Bahar Tercan1, Boris Aguilar1, Sui Huang1

  • 1Institute for Systems Biology, Seattle, WA, USA.

Iscience
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

Computational methods identified key transcription factors (TFs) for cell transdifferentiation. Targeting differentially expressed TFs, like EBF1 and CEBPB, effectively guides cell state changes, such as progenitor B cells to monocytes.

Keywords:
Biological sciencescell engineering

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

858
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.8K

Related Experiment Videos

Last Updated: Aug 29, 2025

Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters
09:22

Rapid Synthesis and Screening of Chemically Activated Transcription Factors with GFP-based Reporters

Published on: November 26, 2013

14.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

858
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.8K

Area of Science:

  • Computational Biology
  • Systems Biology
  • Cellular Reprogramming

Background:

  • Transcription factors (TFs) orchestrate cell identity and differentiation.
  • Understanding TF cross-talk is crucial for directing cell fate decisions.
  • Cell transdifferentiation holds therapeutic potential but requires precise control.

Purpose of the Study:

  • To develop a computational strategy for identifying optimal TF interventions for transdifferentiation.
  • To model hematopoietic TF interactions using probabilistic Boolean networks (PBNs).
  • To predict effective TF manipulations for inducing specific cell state transitions.

Main Methods:

  • Constructed PBNs from single-cell RNA sequencing data representing distinct cell states.
  • Employed a "sampled network" approach to build large-scale TF interaction networks.
  • Simulated interventions by activating/deactivating individual TFs to assess steady-state probability shifts.

Main Results:

  • Confirmed that differentially expressed TFs are prime targets for inducing transdifferentiation.
  • Identified specific TF interventions for progenitor B cell to monocyte transdifferentiation.
  • Key interventions include EBF1 down-regulation, CEBPB up-regulation, TCF3 down-regulation, and STAT3 up-regulation.

Conclusions:

  • Computational PBN modeling effectively predicts TF interventions for transdifferentiation.
  • Targeting specific, differentially expressed TFs is a viable strategy for cell fate engineering.
  • This approach provides a framework for designing targeted therapies in regenerative medicine.