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

Enzyme Inhibition

Enzyme Inhibition

Inhibitors are molecules that reduce enzyme activity by binding to the enzyme. In a normally functioning cell, enzymes are regulated by a variety of inhibitors. Drugs and other toxins can also inhibit enzymes. Some inhibitors bind to the enzyme’s active site, while others inhibit enzymatic activity by binding to other sites on the protein structure.

Feedback Inhibition

Feedback Inhibition

Biochemical reactions are occurring constantly in cells, converting starting substances to different products, usually with the help of enzymes that speed the reactions. Without enzymes, it would take far too long for most reactions to occur to be useful to the cell!

You might also read

Related Articles

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

Sort by

Same author

A Variation of Cover-Free Families and Its Applications.

Journal of computational biology : a journal of computational molecular cell biology·2015

Same author

Identification and classification problems on pooling designs for inhibitor models.

Journal of computational biology : a journal of computational molecular cell biology·2010

Same author

Limit theorems for patterns in phylogenetic trees.

Journal of mathematical biology·2009

Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 12, 2026

A Semi-High-Throughput Adaptation of the NADH-Coupled ATPase Assay for Screening Small Molecule Inhibitors

A Semi-High-Throughput Adaptation of the NADH-Coupled ATPase Assay for Screening Small Molecule Inhibitors

Published on: August 17, 2019

Threshold group testing on inhibitor model.

Huilan Chang¹, Hung-Lin Fu, Chih-Huai Shih

¹Department of Applied Mathematics, National University of Kaohsiung, Kaohsiung, Taiwan. huilan0102@gmail.com

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|April 26, 2013

Summary

This summary is machine-generated.

This study introduces nonadaptive algorithms for threshold group testing with k-inhibitors, considering error tolerance. A two-stage algorithm is presented to identify inhibitors and approximate sets efficiently.

More Related Videos

High-throughput Screening for Small-molecule Modulators of Inward Rectifier Potassium Channels

High-throughput Screening for Small-molecule Modulators of Inward Rectifier Potassium Channels

Published on: January 27, 2013

Related Experiment Videos

Last Updated: May 12, 2026

A Semi-High-Throughput Adaptation of the NADH-Coupled ATPase Assay for Screening Small Molecule Inhibitors

A Semi-High-Throughput Adaptation of the NADH-Coupled ATPase Assay for Screening Small Molecule Inhibitors

Published on: August 17, 2019

High-throughput Screening for Small-molecule Modulators of Inward Rectifier Potassium Channels

High-throughput Screening for Small-molecule Modulators of Inward Rectifier Potassium Channels

Published on: January 27, 2013

Area of Science:

Computer Science
Information Theory
Algorithm Design

Background:

Classical group testing identifies a subset of positive items from a population using group tests.
Threshold group testing generalizes classical methods by defining test outcomes based on the number of positive items.
Inhibitor models introduce special items that can negate test results, complicating identification.

Purpose of the Study:

To develop nonadaptive algorithms for the threshold group testing on k-inhibitor model, incorporating error tolerance.
To design a two-stage algorithm for accurately identifying all inhibitors within the system.
To find a g-approximate set of items efficiently under these complex testing conditions.

Main Methods:

Development of nonadaptive algorithms tailored for the threshold group testing on k-inhibitor model.
Incorporation of error-tolerance mechanisms into the testing and identification algorithms.
Design and implementation of a novel two-stage algorithm for inhibitor identification and set approximation.

Main Results:

Successfully provided nonadaptive algorithms for threshold group testing on k-inhibitor models with error tolerance.
Demonstrated the effectiveness of a two-stage algorithm in identifying all inhibitors.
Achieved a g-approximate set identification, offering a practical solution for complex group testing scenarios.

Conclusions:

The proposed nonadaptive algorithms offer efficient solutions for the complex threshold group testing on k-inhibitor model with error tolerance.
The two-stage algorithm provides a robust method for identifying inhibitors and approximating item sets.
This research advances group testing methodologies, particularly for scenarios involving thresholds and inhibitors.