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

Regulation of Metabolism

Regulation of Metabolism

Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...

What is Gene Expression?

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...

What is Gene Expression?

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

Constitutive and Regulated Gene Expression

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...

Regulation of Expression at Multiple Steps

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...

Amino Acid Biosynthetic Pathways

Amino Acid Biosynthetic Pathways

Amino acid biosynthesis is essential for cell growth, protein synthesis, and metabolic regulation. Cells generate essential and non-essential amino acids from metabolic intermediates to sustain vital biological functions. These intermediates originate from key metabolic pathways: glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway. Important precursors include α-ketoglutarate, pyruvate, oxaloacetate, phosphoenolpyruvate, and erythrose-4-phosphate, which provide...

You might also read

Related Articles

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

Sort by

Same author

Machine-Learning-Guided Discovery of CH<sub>4</sub> Combustion Catalysts Operating in the Presence of SO<sub>2</sub>.

Journal of the American Chemical Society·2026

Same author

Clinical Key Features Uncovered by Blood Eosinophilia-Based Machine Learning Classification of Chronic Rhinosinusitis.

International forum of allergy & rhinology·2025

Same author

Re: Letter to the Editor Regarding "Uncovering Key Features for Predicting Comorbid Chronic Eosinophilic Pneumonia in Chronic Rhinosinusitis Via Machine Learning".

International forum of allergy & rhinology·2025

Same author

Beyond rigid docking: deep learning approaches for fully flexible protein-ligand interactions.

Briefings in bioinformatics·2025

Same author

Development of Highly Active Catalysts for Low-Temperature CO<sub>2</sub> Hydrogenation to Methanol Using a Machine Learning Approach.

Journal of the American Chemical Society·2025

Same author

Data-driven de novo design of super-adhesive hydrogels.

Nature·2025

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 11, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Mining metabolic pathways through gene expression.

Timothy Hancock¹, Ichigaku Takigawa, Hiroshi Mamitsuka

¹Bioinformatics Center, Institute for Chemical Research, Kyoto University, Japan. timhancock@kuicr.kyoto-u.ac.jp

Bioinformatics (Oxford, England)

|July 1, 2010

Summary

This summary is machine-generated.

This study introduces a new computational approach to identify key genetic pathways driving metabolic responses. The method uses probabilistic models and network analysis to uncover biologically meaningful pathways from experimental data.

More Related Videos

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Related Experiment Videos

Last Updated: Jun 11, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Area of Science:

Computational Biology
Systems Biology
Metabolic Network Analysis

Background:

Metabolic responses arise from complex interactions within genetic pathways.
Understanding which specific pathways drive observed metabolic changes remains a challenge due to metabolic network complexity.

Purpose of the Study:

To develop and present a novel computational approach for identifying genetic pathways that dictate metabolic network responses under specific experimental conditions.

Main Methods:

Utilizes a combination of probabilistic models for pathway ranking, clustering, and classification.
Employs non-parametric pathway extraction to identify highly correlated paths.
Applies Markov clustering and classification algorithms for structure extraction within top-ranked pathways.
Incorporates detailed node and edge annotations for tracking genetic dependencies and analyzing interactions.

Main Results:

Successfully identifies biologically meaningful pathways within two microarray expression datasets.
Leverages entire Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic networks for analysis.
Demonstrates the capability to track pathways concerning genetic dependencies and analyze interacting components.

Conclusions:

The proposed approach effectively identifies key genetic pathways influencing metabolic network behavior.
Provides a robust framework for dissecting complex metabolic responses using computational methods.
The method offers insights into the genetic underpinnings of metabolic functions.