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

In-vitro Mutagenesis

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.

You might also read

Related Articles

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

Sort by

Same author

Systematic Evaluation and Application of Single-Nucleotide Polymorphism-Based Genotyping Methods in Varicella-Zoster Virus Molecular Epidemiology Surveillance - China, 2017-2026.

China CDC weekly·2026

Same author

Central Core Engineering of Aromatic Carbonyl Molecules Enables Highly Efficient and Stable Perovskite Solar Cells.

Angewandte Chemie (International ed. in English)·2026

Same author

Dynamic effects of radioactive iodine therapy on gut microbiota and metabolites in patients with papillary thyroid cancer.

Microbiology spectrum·2026

Same author

Genomic landscape and tumor immune microenvironment of osteosarcoma: Bridging mechanistic insights to precision therapeutics.

Bone reports·2026

Same author

High-Performance Infrared Photodetectors Based on Graphene Nanoribbon Vertical Heterojunctions via Dissociated Double-Walled Carbon Nanotubes.

Nanomaterials (Basel, Switzerland)·2026

Same author

PANoptosis in environmental pollutants-induced chronic respiratory diseases.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research·2026

Search research articles

Related Experiment Video

Updated: Apr 12, 2026

Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes

Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes

Published on: September 25, 2013

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning.

Peter X Geng¹, Jiaheng Hou¹, Jinyuan Guo^1,2

¹Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing, 100871, China.

Scientific Reports

|April 10, 2026

Summary

Predicting lethal gene knockouts is crucial for antibiotic discovery. Tripleknock offers a rapid, genome-scale metabolic model-independent method for screening three-gene knockouts, accelerating research.

Keywords:

Antibiotic design Bacteria Deep learning Lethal effect Triple-gene knockout

More Related Videos

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Published on: November 23, 2012

Generation of In-Frame Gene Deletion Mutants in Pseudomonas aeruginosa and Testing for Virulence Attenuation in a Simple Mouse Model of Infection

Generation of In-Frame Gene Deletion Mutants in Pseudomonas aeruginosa and Testing for Virulence Attenuation in a Simple Mouse Model of Infection

Published on: January 8, 2020

Related Experiment Videos

Last Updated: Apr 12, 2026

Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes

Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes

Published on: September 25, 2013

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Published on: November 23, 2012

Generation of In-Frame Gene Deletion Mutants in Pseudomonas aeruginosa and Testing for Virulence Attenuation in a Simple Mouse Model of Infection

Generation of In-Frame Gene Deletion Mutants in Pseudomonas aeruginosa and Testing for Virulence Attenuation in a Simple Mouse Model of Infection

Published on: January 8, 2020

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Investigating multi-gene knockouts is vital for identifying novel antibiotic targets and advancing metabolic engineering.
Experimental screening of three-gene combinations is time-consuming due to complex interactions.
Existing computational methods like Flux Balance Analysis (FBA) require extensive model construction and are slow for large-scale screening.

Purpose of the Study:

To develop a faster, Genome-scale metabolic model (GEM)-independent computational approach for predicting the lethal effects of three-gene knockouts.
To introduce Tripleknock, a novel predictive model for facilitating genome-wide three-gene knockout screening.

Main Methods:

Tripleknock was trained using genome-derived protein sequence features from *Escherichia coli* K-12 MG1655.
Three-gene knockout lethality was simulated using FBA, defining lethal effects by a >=90% reduction in cell growth.
Model performance was evaluated using cross-species F1 scores and benchmarked against essentiality rules and curated literature data.

Main Results:

Tripleknock predicts three-gene knockout lethality approximately 20 times faster than FBA.
The model achieved an average cross-species F1 score of 0.77 on six *Enterobacteriaceae* pathogens.
External validation on *E. coli* triple perturbations showed no false positives among predicted lethal cases (FP=0).

Conclusions:

Tripleknock provides a significantly accelerated and GEM-independent alternative for predicting bacterial triple-gene knockout lethality.
The developed framework establishes a reproducible baseline for evaluating bacterial triple-knockout lethality prediction methods.
This approach facilitates faster discovery of essential genes for antibiotic targets and metabolic engineering applications.