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

You might also read

Related Articles

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

Sort by
Same author

The Next Generation of Protein Sequencing and Analysis Methods.

Annual review of analytical chemistry (Palo Alto, Calif.)·2026
Same author

Author Correction: Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design.

Nature communications·2026
Same author

Structure-informed models for ionic current prediction in nanopore sequencing of expanded dna alphabets.

Nucleic acids research·2025
Same author

Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design.

Nature communications·2025
Same author

Hybridization-encoded DNA tags with paper-based readout for anti-forgery raw material tracking.

Nature communications·2025
Same author

A generative model for inorganic materials design.

Nature·2025

Related Experiment Video

Updated: Jul 11, 2025

Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.4K

Physical Laboratory Automation in Synthetic Biology.

Ashley Stephenson1,2, Lauren Lastra2, Bichlien Nguyen2

  • 1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.

ACS Synthetic Biology
|November 7, 2023
PubMed
Summary
This summary is machine-generated.

Synthetic biology is advancing with Design-Build-Test-Learn (DBTL) automation, but connectivity and access challenges hinder progress. The field focuses on physical workflow automation, balancing human involvement with intelligent systems.

Keywords:
automationdesign-build-test-learnmicrofluidicsroboticsstandardizationsynthetic biology

More Related Videos

Rapid Characterization of Genetic Parts with Cell-Free Systems
05:00

Rapid Characterization of Genetic Parts with Cell-Free Systems

Published on: August 30, 2021

1.9K
Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials
10:28

Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials

Published on: March 9, 2017

9.0K

Related Experiment Videos

Last Updated: Jul 11, 2025

Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

12.4K
Rapid Characterization of Genetic Parts with Cell-Free Systems
05:00

Rapid Characterization of Genetic Parts with Cell-Free Systems

Published on: August 30, 2021

1.9K
Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials
10:28

Using Synthetic Biology to Engineer Living Cells That Interface with Programmable Materials

Published on: March 9, 2017

9.0K

Area of Science:

  • Synthetic Biology
  • Biotechnology
  • Automation Engineering

Background:

  • Synthetic biology is transitioning to a systems era, adopting Design-Build-Test-Learn (DBTL) methodologies.
  • The imperative for automation and standardization in synthetic biology research is widely recognized for reproducibility and scalability.

Purpose of the Study:

  • To review and classify the current state of automation in synthetic biology, emphasizing physical experimental workflows.
  • To identify challenges in lab automation, specifically connectivity and adoption barriers within DBTL modules.

Main Methods:

  • Characterization and classification of existing automation technologies in synthetic biology.
  • Analysis of hardware and software tools supporting automated experimental workflows.
  • Discussion on the balance between human intervention and full automation in scientific discovery.

Main Results:

  • Significant progress has been made in automating key experimental processes through integrated hardware and software solutions.
  • Despite advancements, a lack of seamless connectivity between DBTL modules presents a major hurdle.
  • Barriers to access and adoption of automation tools limit the realization of its full potential.

Conclusions:

  • While fully autonomous scientific discovery remains distant, substantial strides toward automating experimental elements are evident.
  • Further development is crucial in enhancing connectivity and accessibility of automation tools.
  • The optimal goal may involve appropriate automation rather than complete human removal from the scientific process.