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

Reinforcement

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:

Bioremediation

Bioremediation

Bioremediation is the use of prokaryotes, fungi, or plants to remove pollutants from the environment. This process has been used to remove harmful toxins in groundwater as a byproduct of agricultural run-off and also to clean up oil spills.

Reinforcement Schedules

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...

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...

You might also read

Related Articles

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

Sort by

Same author

Active-learning-guided optimization of cell-free systems for genome-wide transcriptomic profiling reveals progressive layers of regulation.

Nature communications·2026

Same author

RetroRules 2026: an expanded database combining biochemical and organic reaction templates for pathway discovery.

Nucleic acids research·2025

Same author

Reverse engineering molecules from fingerprints through deterministic enumeration and generative models.

Journal of cheminformatics·2025

Same author

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.

ACS synthetic biology·2024

Same author

neo4jsbml: import systems biology markup language data into the graph database Neo4j.

PeerJ·2024

Same author

A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models.

Nature communications·2023

Same journal

Breaking the Stability-Activity-Selectivity Trilemma in Unspecific Peroxygenase through Computation-Based Cross-Regional Combinatorial Mutagenesis.

ACS synthetic biology·2026

Same journal

Sequential Plasmid Curing and Genome Editing in <i>Escherichia coli</i> Nissle 1917.

ACS synthetic biology·2026

Same journal

An Explainable Deep Learning Framework Integrating DNA Sequence and Transcription Initiation Signals for Gene Expression Prediction.

ACS synthetic biology·2026

Same journal

A Multitask Prediction Framework for CircRNAs, Drugs, and Diseases Based on Multi-View Information Integration and Graph Contrastive Learning.

ACS synthetic biology·2026

Same journal

Engineering Modular Cargo Loading Strategies for Carboxysome-Derived Protein Particles.

ACS synthetic biology·2026

Same journal

Suppression of Salmonella Effectors with CRISPRi Controls the Immune Response to Bacterial Therapies.

ACS synthetic biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 1, 2026

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Reinforcement Learning for Bioretrosynthesis.

Mathilde Koch¹, Thomas Duigou¹, Jean-Loup Faulon^1,2,3

¹Micalis Institute, INRA, AgroParisTech , Université Paris-Saclay , 78350 Jouy-en-Josas , France.

ACS Synthetic Biology

|December 17, 2019

Summary

This summary is machine-generated.

This study introduces RetroPath RL, an AI tool using Monte Carlo Tree Search for metabolic engineering. It accelerates the discovery of greener chemical production pathways by exploring bioretrosynthesis.

Keywords:

Monte Carlo Tree Search metabolic engineering pathway design reinforcement learning retrosynthesis

More Related Videos

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

Pavlovian Conditioned Approach Training in Rats

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

Related Experiment Videos

Last Updated: Jan 1, 2026

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

Pavlovian Conditioned Approach Training in Rats

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

Area of Science:

Biotechnology
Computational Biology
Synthetic Biology

Background:

Metabolic engineering seeks sustainable chemical production but faces long, costly R&D cycles.
Efficient computational tools are crucial for navigating the vast chemical biosynthetic space.
Current methods require improvement for exploring bioretrosynthesis pathways.

Purpose of the Study:

To develop an AI-driven tool for exploring bioretrosynthesis.
To accelerate the design of metabolic engineering pathways.
To reduce the cost and time associated with discovering new chemical production routes.

Main Methods:

Utilized artificial intelligence, specifically Monte Carlo Tree Search (MCTS) reinforcement learning.
Employed chemical similarity as a guiding principle within the MCTS algorithm.
Implemented the approach in an open-source command-line tool named RetroPath RL.
Developed a feature for suggesting media supplements for enzymatic synthesis.

Main Results:

Validated RetroPath RL on a curated dataset of 20 experimental pathways.
Demonstrated efficacy on a larger dataset comprising 152 successful metabolic engineering projects.
Successfully explored the bioretrosynthesis space, identifying potential production pathways.

Conclusions:

RetroPath RL offers an efficient AI-based solution for metabolic engineering pathway design.
The tool aids in accelerating the discovery of greener chemical production methods.
The inclusion of media supplement suggestions enhances the practical applicability of the tool.