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 Experiment Videos

Refactoring bacteriophage T7.

Leon Y Chan1, Sriram Kosuri, Drew Endy

  • 1Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Molecular Systems Biology
|May 27, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Deep mutational scanning reveals pharmacologically relevant insights into TYK2 signaling and disease.

eLife·2026
Same author

Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.

Nature communications·2026
Same author

Blind Challenges Let Us See the Path Forward for Predictive Models.

Journal of chemical information and modeling·2026
Same author

Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research.

Nature machine intelligence·2025
Same author

Covalent Degraders of Immune Regulatory Transcription Factors IRF8 and IRF5.

bioRxiv : the preprint server for biology·2025
Same author

High-resolution deep mutational scanning of the melanocortin-4 receptor enables target characterization for drug discovery.

eLife·2025
Same journal

Common xenobiotics modulate gut microbial responses to low‑calorie sweeteners in vitro.

Molecular systems biology·2026
Same journal

ParTIpy: a scalable framework for archetypal analysis and Pareto task inference.

Molecular systems biology·2026
Same journal

Quantitative interactome mapping of skeletal muscle insulin resistance.

Molecular systems biology·2026
Same journal

Interpretable multi-omics integration across mixed-order tensors with MANTRA.

Molecular systems biology·2026
Same journal

To cleave or not to cleave: a systemic evaluation of DSS versus DSSO for cross-linking mass spectrometry analysis.

Molecular systems biology·2026
Same journal

Multiscale learning of gene network-driven phenotypic dynamics of single cells.

Molecular systems biology·2026
See all related articles

Scientists engineered a simpler bacteriophage T7 genome for easier study and manipulation. This redesigned genome is viable, demonstrating that natural biological systems can be rebuilt for scientific advancement.

Area of Science:

  • Synthetic Biology
  • Genomics
  • Molecular Biology

Background:

  • Natural biological systems, shaped by evolution, often present complex structures that hinder scientific study and modification.
  • Bacteriophage T7, a well-studied virus, serves as a model system, yet its genome's complexity poses challenges for detailed analysis and engineering.

Purpose of the Study:

  • To redesign the bacteriophage T7 genome to create a simpler, more manageable surrogate for enhanced research.
  • To test the hypothesis that overlapping genetic elements are nonessential for viral viability and that functional models are sufficient for redesign.

Main Methods:

  • Engineered a chimeric genome by replacing the initial 11,515 base pairs (bp) of the wild-type bacteriophage T7 genome (39,937 bp) with 12,179 bp of synthetic DNA.
  • Assessed the viability of the resulting bacteriophage with the engineered genome.

Related Experiment Videos

Main Results:

  • Successfully created a viable bacteriophage with a redesigned, chimeric genome.
  • The engineered bacteriophage retained key characteristics of the wild-type while exhibiting simplified modeling and manipulation potential.

Conclusions:

  • The systematic redesign and reconstruction of natural biological system genomes are feasible for scientific understanding and intentional applications.
  • This work validates the approach of simplifying complex genomes to facilitate further research and synthetic biology applications.