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Related Concept Videos

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...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Related Experiment Video

Updated: May 14, 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

RegVelo: Gene-regulatory-informed dynamics of single cells.

Weixu Wang1, Zhiyuan Hu2, Philipp Weiler3

  • 1Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.

Cell
|May 12, 2026
PubMed
Summary

RegVelo, a new deep learning framework, integrates RNA splicing and gene regulatory networks to predict cell fate. This approach reveals key regulators like tfec and elf1 in zebrafish neural crest development.

Keywords:
cell fate decisiondeep generative modelingearly driversgene regulatory networkin silico perturbationin vivo Perturb-seqmechanistic modelingregulatory dynamicstranscriptional dynamicszebrafish neural crest

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Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis

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An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions
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An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

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Related Experiment Videos

Last Updated: May 14, 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

Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis
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Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis

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An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions
07:59

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Published on: March 22, 2018

Area of Science:

  • Developmental Biology
  • Computational Biology
  • Genomics

Background:

  • Cell fate transitions are crucial in development but current models like RNA velocity and gene regulatory network (GRN) inference have limitations.
  • RNA velocity models dynamics but lacks gene regulatory insights, while GRN inference methods often ignore system dynamics.

Purpose of the Study:

  • To bridge the gap between RNA velocity and GRN inference by developing a novel computational framework.
  • To create an interpretable deep learning model that jointly analyzes splicing kinetics and gene regulatory interactions for mechanistic insights into cell fate decisions.

Main Methods:

  • Developed RegVelo, a deep learning framework integrating splicing kinetics and gene regulatory interactions.
  • Applied RegVelo to zebrafish neural crest development using Smart-seq3 data, including gene expression and chromatin accessibility.
  • Utilized in silico perturbations, CRISPR-Cas9 knockout, and single-cell Perturb-seq for validation.

Main Results:

  • RegVelo demonstrated reliable predictive power for terminal cell states, gene interactions, and perturbation outcomes across various biological systems.
  • Delineated regulatory programs governing cell fate specification in zebrafish neural crest development.
  • Identified tfec as an early driver and elf1 as a regulator of pigment cell fate, validated experimentally.

Conclusions:

  • RegVelo provides a quantitative framework for integrating gene regulation and cell fate dynamics.
  • The study advances mechanistic understanding of cell fate decisions by combining kinetic and regulatory information.
  • RegVelo offers a powerful tool for analyzing complex biological systems and predicting developmental trajectories.