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

Synthetic Biology

Synthetic Biology

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...

What is Genetic Engineering?

What is Genetic Engineering?

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

Deep learning-enabled discovery of antibiotics effective against <i>Neisseria gonorrhoeae</i>.

Science translational medicine·2026

Same author

Stochastic modeling of epigenetic memory.

NPJ systems biology and applications·2026

Same author

Resource competition shapes CRISPR-mediated gene activation.

Cell systems·2026

Same author

Generative AI for synthetic biology: Designing biological parts, circuits, and genomes.

Cell systems·2026

Same author

What problem do you hope bioengineering or synthetic biology approaches will enable us to tackle in the next decade?

Cell systems·2026

Same author

FATE-MAP predicts teratogenicity and human gastrulation failure modes by integrating deep learning and mechanistic modeling.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: May 30, 2025

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

Machine learning for synthetic gene circuit engineering.

Sebastian Palacios¹, James J Collins², Domitilla Del Vecchio³

¹Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Current Opinion in Biotechnology

|January 28, 2025

Summary

Machine learning (ML) is emerging as a powerful tool to enhance synthetic gene circuit engineering in synthetic biology. Integrating ML with mechanistic models promises to overcome challenges and advance the field.

More Related Videos

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

Related Experiment Videos

Last Updated: May 30, 2025

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Rapid Development of Cell State Identification Circuits with Poly-Transfection

Published on: February 24, 2023

Area of Science:

Synthetic biology
Bioengineering
Computational biology

Background:

Synthetic biology applies engineering principles to program biological systems.
Synthetic gene circuits are engineered biological systems for specific functions.
Designing these circuits involves complex interactions and large design spaces.

Purpose of the Study:

To discuss the emerging role of machine learning (ML) in synthetic gene circuit engineering.
To highlight how ML can enhance circuit design from component to system level.
To explore hybrid approaches combining ML with mechanistic modeling.

Main Methods:

Review and discussion of machine learning applications in synthetic biology.
Analysis of challenges in integrating ML with biological systems.
Exploration of hybrid data-driven and mechanism-based modeling strategies.

Main Results:

Machine learning offers significant potential to improve the design and optimization of synthetic gene circuits.
Hybrid approaches combining ML with mechanistic models can leverage the strengths of both data-driven and physics-based methods.
Several challenges remain in the effective implementation and integration of ML in synthetic biology.

Conclusions:

Machine learning is poised to significantly advance synthetic biology and gene circuit engineering.
Overcoming current challenges is crucial for realizing the full potential of ML in this field.
Hybrid modeling approaches represent a promising direction for future research and development.