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

Combinatorial Gene Control02:33

Combinatorial Gene Control

9.5K
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...
9.5K
siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

18.4K
Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
18.4K
Targeted siRNA Delivery to Neurons for Prion Protein Silencing Using Liposomal Complexes02:57

Targeted siRNA Delivery to Neurons for Prion Protein Silencing Using Liposomal Complexes

336
Source: Bender, H. R., et.al. Delivery of Therapeutic siRNA to the CNS Using Cationic and Anionic Liposomes. J. Vis. Exp. (2016)This video demonstrates the targeted delivery of prion protein (PrP) siRNA to neurons using liposomal siRNA-peptide complexes (LSPCs). Cationic liposomes electrostatically bind PrP-targeting siRNA, which is further stabilized by the RVG-9r peptide for receptor-mediated transport across the blood-brain barrier. Once inside the brain, LSPCs undergo endocytosis in...
336
Bilayer Microfluidic Device for Combinatorial Plug Production07:03

Bilayer Microfluidic Device for Combinatorial Plug Production

1.4K
The fabrication of a polydimethylsiloxane (PDMS)-based bilayer device for the production of combinatorial libraries in water-in-oil emulsions (plugs) is presented here. The necessary hardware and software required to automate plug production are detailed in the protocol, and the production of a quantitative library of fluorescent plugs is also demonstrated.
1.4K
Preparation of Exosomes for siRNA Delivery to Cancer Cells09:59

Preparation of Exosomes for siRNA Delivery to Cancer Cells

26.0K
An exosome is a new generation of drug delivery carriers. We established an exosome isolation protocol with high yield and purity for siRNA delivery. We also encapsulated fluorescently labelled non-specific siRNA into exosomes and investigated the cellular uptake of siRNA-loaded exosomes in cancer...
26.0K
siRNA Encapsulation into Exosomes Using Electroporation: An Electric Pulse Based Technique to Load siRNA into Exosomes03:04

siRNA Encapsulation into Exosomes Using Electroporation: An Electric Pulse Based Technique to Load siRNA into Exosomes

3.7K
This video describes an electroporation-based method for encapsulating siRNA into exosomes. The siRNA-loaded exosomes can then be used for therapeutic...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Pathogen-specific host responses define distinct pneumonia endotypes in the human lung.

bioRxiv : the preprint server for biology·2026
Same author

In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomes.

Scientific reports·2026
Same author

Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.

Cell reports methods·2025
Same author

DelSIEVE: cell phylogeny modeling of single nucleotide variants and deletions from single-cell DNA sequencing data.

Genome biology·2025
Same author

AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.

Accounts of chemical research·2025
Same author

Author Correction: Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope.

Nature communications·2025

Related Experiment Video

Updated: Jan 19, 2026

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

1.4K

Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects.

Jerzy Tiuryn1, Ewa Szczurek1

  • 1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.

Bioinformatics (Oxford, England)
|September 13, 2019
PubMed
Summary

This study introduces enhanced linear effects models (LEMs) to accurately infer cellular signaling networks from perturbation data, accounting for complex gene interactions and off-target effects in RNA interference experiments.

More Related Videos

Targeted siRNA Delivery to Neurons for Prion Protein Silencing Using Liposomal Complexes
02:57

Targeted siRNA Delivery to Neurons for Prion Protein Silencing Using Liposomal Complexes

Published on: June 18, 2025

336
Combinatorial Gene Control: Synergistic Action of Transcription Factors
02:33

Combinatorial Gene Control: Synergistic Action of Transcription Factors

9.5K

Related Experiment Videos

Last Updated: Jan 19, 2026

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology
07:03

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

1.4K
Targeted siRNA Delivery to Neurons for Prion Protein Silencing Using Liposomal Complexes
02:57

Targeted siRNA Delivery to Neurons for Prion Protein Silencing Using Liposomal Complexes

Published on: June 18, 2025

336
Combinatorial Gene Control: Synergistic Action of Transcription Factors
02:33

Combinatorial Gene Control: Synergistic Action of Transcription Factors

9.5K

Area of Science:

  • Computational biology
  • Systems biology
  • Network inference

Background:

  • Perturbation experiments are crucial for studying cellular networks but are complicated by factors like RNA interference (RNAi) off-target effects.
  • These effects lead to combinatorial gene perturbations and signal propagation through unknown network connections, complicating computational modeling.
  • Understanding how many and which perturbations are needed to accurately infer signaling network models is a key challenge.

Purpose of the Study:

  • To introduce an enhanced version of linear effects models (LEMs) capable of accounting for both positive and negative contributions of perturbed proteins.
  • To determine the minimal perturbation combinations required for unique and accurate network model identification.
  • To improve the accuracy of parameter estimation and network structure learning from perturbation data.

Main Methods:

  • Developed an enhanced linear effects model (LEM) incorporating positive and negative protein contributions.
  • Proved identifiability of enhanced LEMs using data from single, pair, and triplet protein perturbations.
  • Validated the model's performance through extensive simulations and application to real-world RNAi screening data.

Main Results:

  • Enhanced LEMs are identifiable with perturbations of single, pairs, and triplets; only single and pairs are needed for small networks (≤5 nodes).
  • Extensive simulations show enhanced LEMs achieve high accuracy in parameter estimation and network structure learning, outperforming previous models.
  • Application to Bartonella henselae infection data identified known interactions and suggested a novel interaction.

Conclusions:

  • Enhanced LEMs provide a robust framework for inferring signaling networks from complex perturbation data.
  • The model accurately estimates parameters and learns network structures, even with RNAi off-target effects.
  • This approach advances the computational modeling of cellular signaling pathways and aids in discovering biological interactions.