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...

You might also read

Related Articles

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

Sort by

Same author

Bacterial gene regulatory neural network as a biocomputing library of mathematical solvers.

NPJ systems biology and applications·2026

Same author

<i>In silico</i> modelling of neuron signal impact of cytokine storm-induced demyelination.

Open biology·2024

Same author

Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network.

Biophysical reports·2024

Same author

Revealing gene regulation-based neural network computing in bacteria.

Biophysical reports·2023

Same author

BioSIMP: Using Software Testing Techniques for Sampling and Inference in Biological Organisms.

SE4Science 2017 : 2017 IEEE/ACM 12th International Workshop on Software Engineering for Science : proceedings : 22 May 2017, Buenos Aires, Argentina. International Workshop on Software Engineering for Science (2017 : Buenos Aires, Argen...·2023

Same author

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions.

IEEE transactions on molecular, biological, and multi-scale communications·2022

Same journal

Smart Ultrasound-Responsive Nanoparticles for Drug Delivery.

IEEE nanotechnology magazine·2026

Same journal

Cytomorphic Electronic Systems: A review and perspective.

IEEE nanotechnology magazine·2022

Same journal

Novel Nanoplasmonic-Structure-Based Integrated Microfluidic Biosensors for Label-Free in Situ Immune Functional Analysis: A review of recent progress.

IEEE nanotechnology magazine·2021

Same journal

Nanocrystalline Diamond Electrodes: Enabling electrochemical microsensing applications with high reliability and stability.

IEEE nanotechnology magazine·2020

Same journal

Candle Soot Carbon Nanoparticles in Photoacoustics: Advantages and Challenges for Laser Ultrasound Transmitters.

IEEE nanotechnology magazine·2019

Same journal

Modeling Transport Through Synthetic Nanopores.

IEEE nanotechnology magazine·2011

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges.

Sasitharan Balasubramaniam¹, Samitha Somathilaka^1,2, Sehee Sun¹

¹School of Computing, University of Nebraska-Lincoln, NE, USA.

IEEE Nanotechnology Magazine

|June 10, 2024

Summary

This summary is machine-generated.

Molecular Machine Learning (MML) explores using molecules for computation, moving beyond traditional devices. This research reviews MML approaches and proposes novel directions using biological systems for AI tasks.

Keywords:

Artificial Intelligence Machine Learning Molecular Communications Synthetic Biology

More Related Videos

Designing a Bio-responsive Robot from DNA Origami

Designing a Bio-responsive Robot from DNA Origami

Published on: July 8, 2013

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Jun 24, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Designing a Bio-responsive Robot from DNA Origami

Designing a Bio-responsive Robot from DNA Origami

Published on: July 8, 2013

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Biotechnology
Artificial Intelligence
Molecular Computing

Background:

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into daily life and devices.
Energy-efficient algorithms are crucial for low-power devices, driving innovation in computational methods.

Purpose of the Study:

To investigate Molecular Machine Learning (MML) as a novel approach for performing machine learning functions at the molecular scale.
To explore new directions for MML, including the use of gene regulatory networks and biological systems.

Main Methods:

Review of current MML approaches.
Investigation of gene regulatory networks and population interactions for creating neural networks.
Exploration of calcium signaling mechanisms for training biological machine learning structures.

Main Results:

Demonstration of MML principles through molecular transport, processing, and interpretation via chemical reactions.
Proposal of novel MML architectures based on biological systems.
Successful application of MML in biological cells to construct an Analog to Digital Converter (ADC).

Conclusions:

MML offers a promising avenue for developing highly efficient and miniaturized machine learning systems.
Biological systems, such as gene regulatory networks and cellular signaling, provide a viable platform for implementing MML.
Future research in MML holds potential for solving complex computational challenges and advancing AI capabilities.