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

Synthetic Biology02:55

Synthetic Biology

4.7K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
4.7K

You might also read

Related Articles

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

Sort by
Same author

Flux sampling and graph neural networks for improved gene essentiality prediction in mammalian genome-scale metabolic models.

NPJ systems biology and applications·2026
Same author

Knowledge preservation in the era of big science and AI: strategies for sustainable scientific research.

Nature communications·2026
Same author

Leveraging population-scale proteomic data with deep learning for head and neck cancer detection in saliva.

NPJ digital medicine·2026
Same author

CLIMBS: Assessing Carbohydrate-Protein Interactions through a Graph Neural Network Classifier Using Synthetic Negative Data.

Journal of chemical information and modeling·2026
Same author

Analysis and control of untemplated DNA polymerase activity for guided synthesis of kilobase-scale DNA sequences.

Nature communications·2026
Same author

The Next Generation of Protein Sequencing and Analysis Methods.

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

Related Experiment Video

Updated: Jun 21, 2025

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

1.8K

Data hazards in synthetic biology.

Natalie R Zelenka1,2, Nina Di Cara3, Kieren Sharma4

  • 1Jean Golding Institute, University of Bristol, Bristol, UK.

Synthetic Biology (Oxford, England)
|July 8, 2024
PubMed
Summary

Data science in engineered biology presents risks like data bias and environmental impact. A new framework helps assess these data hazards, ensuring trustworthy AI applications in synthetic biology.

Keywords:
AIdata hazardsdata scienceethicssynthetic biology

More Related Videos

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
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.2K

Related Experiment Videos

Last Updated: Jun 21, 2025

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

1.8K
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
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.2K

Area of Science:

  • Engineered biology
  • Synthetic biology
  • Data science applications

Background:

  • High-throughput methods (e.g., massively parallel reporter assays, advanced microscopy, computational protein design) and whole-cell models generate vast datasets in engineered biology.
  • The increasing use of data-centric analyses in synthetic biology raises concerns about potential risks, including data bias and environmental impact.

Purpose of the Study:

  • To present a community-developed framework for assessing data hazards in engineered biology.
  • To address concerns regarding data bias and the environmental impact of large-scale data analyses.
  • To provide guidelines and mitigating steps for responsible data use in synthetic biology.

Main Methods:

  • Development of a community-driven framework for evaluating data hazards.
  • Application of the framework to two synthetic biology case studies.
  • Analysis of common bioengineering projects to identify data-related considerations.

Main Results:

  • Demonstrated the framework's applicability across diverse bioengineering projects.
  • Highlighted the variety of data hazard considerations encountered in synthetic biology.
  • Identified potential biases in underlying data and environmental impacts of data analysis.

Conclusions:

  • Proactive assessment and mitigation of data hazards are crucial for the responsible application of data-intensive AI in synthetic biology.
  • The developed framework enhances the trustworthiness of AI methods in engineered biology.
  • Understanding and addressing data-related risks are essential for advancing synthetic biology.