Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

You might also read

Related Articles

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

Sort by

Same author

Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer.

Scientific reports·2026

Same author

GeneSNAKE: a Python package for simulation of gene regulatory networks and perturbation-induced expression data.

Bioinformatics advances·2026

Same author

scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes.

bioRxiv : the preprint server for biology·2026

Same author

<i>MapMyCells:</i> High-performance mapping of unlabeled cell-by-gene data to reference brain taxonomies.

bioRxiv : the preprint server for biology·2026

Same author

Enhancer dynamics and cellular architecture in the human spinal cord.

Neuron·2026

Same author

Involvement of Adenosine A<sub>2</sub>A Receptors in Anxiety-Like Behaviors in Tetrahydrocannabinol-Treated Mice.

Brain and behavior·2026

Same journal

An epigenetic clock for chronological age estimation in East Asian populations.

NAR genomics and bioinformatics·2026

Same journal

The role of ATF4 in neurons under mitochondrial stress.

NAR genomics and bioinformatics·2026

Same journal

Distinct repeat architecture landscapes in the proteomes of protozoan parasites.

NAR genomics and bioinformatics·2026

Same journal

Long non-coding RNA triplex-dependent regulation of melanoma gene networks.

NAR genomics and bioinformatics·2026

Same journal

Challenges in predicting chromatin accessibility differences between species.

NAR genomics and bioinformatics·2026

Same journal

Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions.

NAR genomics and bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data.

Mateusz Garbulowski^1,2, Thomas Hillerton¹, Daniel Morgan¹

¹Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, Solna 171 21, Sweden.

NAR Genomics and Bioinformatics

|September 19, 2024

Summary

This summary is machine-generated.

GeneSPIDER2 enhances gene regulatory network (GRN) analysis by simulating single-cell data, including genetic perturbations. This updated toolbox generates large, realistic GRNs and validates synthetic data against real Perturb-seq experiments.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Related Experiment Videos

Last Updated: Jun 17, 2026

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Single-cell data is crucial for gene regulatory network (GRN) inference, with benchmarks relying on simulated data.
Existing single-cell simulators lack the capability to model gene perturbations, a key biological process.
Generating large-scale GRNs presents computational and stability challenges.

Purpose of the Study:

To introduce GeneSPIDER2, an updated MATLAB toolbox for GRN benchmarking, inference, and analysis.
To enhance the generation of large-scale GRNs with realistic topological properties.
To enable simulation of single-cell data, including unique features for modeling genetic perturbations.

Main Methods:

Developed improved software modules within GeneSPIDER2 for enhanced capabilities and performance.
Implemented algorithms for generating large GRNs with scale-free degree distribution and modularity.
Introduced a novel simulation module for generating single-cell data based on genetic perturbations.

Main Results:

GeneSPIDER2 can generate large GRNs exhibiting biologically realistic topological features.
The toolbox successfully simulates single-cell data, incorporating the effects of genetic perturbations.
Simulated single-cell data demonstrated similar properties to real Perturb-seq data from two cell lines.

Conclusions:

GeneSPIDER2 provides a robust platform for GRN inference and analysis using simulated single-cell data.
The ability to simulate gene perturbations offers a unique advantage for benchmarking GRN inference methods.
The validation against real data confirms the utility of GeneSPIDER2 for generating realistic synthetic single-cell datasets.