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

CRISPR and crRNAs02:53

CRISPR and crRNAs

16.9K
Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...
16.9K
CRISPR01:59

CRISPR

50.1K
Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced...
50.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
35

You might also read

Related Articles

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

Sort by
Same author

Convergent mechanisms in Wnt and Hedgehog signaling.

Science signaling·2026
Same author

Combinatorial and Inducible CRISPRa/i Enables Canalized hiPSC Forward Programming and Iterative Refinement <i>via</i> Single-Cell Genomics.

bioRxiv : the preprint server for biology·2026
Same author

Membrane Composition Influences Expression Yield of Plant Cytochrome P450s in <i>E. coli</i> Lysate-Based Cell-Free Systems.

ACS synthetic biology·2026
Same author

A standardized workflow for kinetic metabolic model curation and dissemination.

PLoS computational biology·2026
Same author

Antimony 3: Extending human-readable model definitions for SBML Level 3 Core and Packages.

bioRxiv : the preprint server for biology·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026

Related Experiment Video

Updated: Jun 17, 2025

CRISPR/Cas12a Multiplex Genome Editing of Saccharomyces cerevisiae and the Creation of Yeast Pixel Art
10:18

CRISPR/Cas12a Multiplex Genome Editing of Saccharomyces cerevisiae and the Creation of Yeast Pixel Art

Published on: May 28, 2019

17.0K

Systems-Level Modeling for CRISPR-Based Metabolic Engineering.

Ryan A L Cardiff1,2,3, James M Carothers1,3, Jesse G Zalatan1,2

  • 1Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, Washington 98195, United States.

ACS Synthetic Biology
|August 9, 2024
PubMed
Summary
This summary is machine-generated.

CRISPR-Cas systems enhance metabolic engineering by guiding gene activation or repression. Improved models and machine learning will boost CRISPR-Cas9 tool efficacy for optimizing microbial biosynthetic pathways.

Keywords:
CRISPRgRNA designgenome-scale modelingmetabolic engineering

More Related Videos

Efficient Production and Identification of CRISPR/Cas9-generated Gene Knockouts in the Model System Danio rerio
11:27

Efficient Production and Identification of CRISPR/Cas9-generated Gene Knockouts in the Model System Danio rerio

Published on: August 28, 2018

22.0K
A New Toolkit for Evaluating Gene Functions using Conditional Cas9 Stabilization
08:20

A New Toolkit for Evaluating Gene Functions using Conditional Cas9 Stabilization

Published on: September 2, 2021

4.1K

Related Experiment Videos

Last Updated: Jun 17, 2025

CRISPR/Cas12a Multiplex Genome Editing of Saccharomyces cerevisiae and the Creation of Yeast Pixel Art
10:18

CRISPR/Cas12a Multiplex Genome Editing of Saccharomyces cerevisiae and the Creation of Yeast Pixel Art

Published on: May 28, 2019

17.0K
Efficient Production and Identification of CRISPR/Cas9-generated Gene Knockouts in the Model System Danio rerio
11:27

Efficient Production and Identification of CRISPR/Cas9-generated Gene Knockouts in the Model System Danio rerio

Published on: August 28, 2018

22.0K
A New Toolkit for Evaluating Gene Functions using Conditional Cas9 Stabilization
08:20

A New Toolkit for Evaluating Gene Functions using Conditional Cas9 Stabilization

Published on: September 2, 2021

4.1K

Area of Science:

  • Synthetic Biology
  • Molecular Biology
  • Biotechnology

Background:

  • CRISPR-Cas systems enable precise gene regulation for metabolic engineering.
  • Optimizing microbial biosynthetic pathways requires advanced transcriptional programming models.
  • Current limitations exist in predicting guide RNA efficacy for gene targeting.

Purpose of the Study:

  • To review advancements in modeling approaches for CRISPR-Cas-mediated metabolic engineering.
  • To highlight the potential of new CRISPR activation (CRISPRa) and interference (CRISPRi) strategies.
  • To emphasize the role of machine learning in enhancing CRISPR-Cas tool capabilities.

Main Methods:

  • Review of genome-scale and flux balance models for identifying CRISPR targets.
  • Discussion of guide RNA prediction models for improved targeting efficacy.
  • Exploration of machine learning integration for CRISPR-Cas systems.

Main Results:

  • Genome-scale and flux balance models have identified targets for improving yields via CRISPRi.
  • New tunable and dynamic CRISPRa approaches promise enhanced engineering capabilities.
  • Guide RNA prediction models increase the efficacy of gene targeting.

Conclusions:

  • Improved models and machine learning are crucial for advancing CRISPR-Cas tools in metabolic engineering.
  • Enhanced CRISPR-Cas capabilities will significantly expand applications in microbial systems.
  • The integration of advanced modeling will overcome current limitations and optimize biosynthetic pathways.