Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

7.4K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
7.4K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

2.6K
2.6K
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

8.8K
Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
8.8K
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

3.1K
3.1K
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

2.7K
2.7K
Competition02:34

Competition

24.9K
When organisms require the same limited resources within an environment, they may have to compete for them. Competition is a net-negative interaction. Even if two competing individuals or populations do not interact directly, the overall fitness of both competitors is lowered as a result of not having full access to the limited resource.
24.9K

You might also read

Related Articles

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

Sort by
Same author

Spin radical enhanced magnetocapacitance effect in intermolecular excited states.

The journal of physical chemistry. B·2013
Same author

Recent developments in stir bar sorptive extraction.

Analytical and bioanalytical chemistry·2013
Same author

Discovery of MK-8742: an HCV NS5A inhibitor with broad genotype activity.

ChemMedChem·2013
Same author

Magnetic polycarbonate microspheres for tumor-targeted delivery of tumor necrosis factor.

Drug delivery·2013
Same author

A study on validity of cortical alpha connectivity for schizophrenia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2013
Same author

Myosin light chain 2-based selection of human iPSC-derived early ventricular cardiac myocytes.

Stem cell research·2013

Related Experiment Video

Updated: Feb 8, 2026

Primer-Free Aptamer Selection Using A Random DNA Library
11:14

Primer-Free Aptamer Selection Using A Random DNA Library

Published on: July 26, 2010

25.4K

Consensus Problems Over Cooperation-Competition Random Switching Networks With Noisy Channels.

Yonghong Wu, Bin Hu, Zhi-Hong Guan

    IEEE Transactions on Neural Networks and Learning Systems
    |July 12, 2018
    PubMed
    Summary

    This study develops distributed iterative algorithms for consensus in multiagent networks with noisy, cooperative, or competitive interactions. The algorithms ensure networks achieve consensus, even with conflicting information, demonstrating effectiveness through simulations.

    More Related Videos

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.2K
    Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila
    05:27

    Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila

    Published on: July 26, 2024

    927

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    Primer-Free Aptamer Selection Using A Random DNA Library
    11:14

    Primer-Free Aptamer Selection Using A Random DNA Library

    Published on: July 26, 2010

    25.4K
    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.2K
    Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila
    05:27

    Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila

    Published on: July 26, 2024

    927

    Area of Science:

    • Control Theory
    • Network Science
    • Distributed Systems

    Background:

    • Consensus problems in multiagent networks are crucial for coordinated behavior.
    • Real-world networks involve noisy communication channels and complex agent interactions (cooperation/competition).
    • Existing algorithms often struggle with the combined challenges of noise and mixed interactions.

    Purpose of the Study:

    • To develop and analyze distributed iterative algorithms for achieving consensus in multiagent networks.
    • To address scenarios with random, noisy communication links between agents.
    • To investigate consensus under both cooperative and competitive neighbor relationships.

    Main Methods:

    • Application of random graph theory to model network topology and random agent interactions.
    • Utilization of stochastic stability theory to analyze system behavior under uncertainty.
    • Design of control gains to ensure convergence properties of the distributed algorithms.

    Main Results:

    • The proposed algorithms enable multiagent networks to reach almost sure consensus.
    • Mean square consensus is achieved even with noisy communication and mixed cooperative-competitive interactions.
    • Control gain design effectively manages network dynamics for robust consensus.

    Conclusions:

    • Distributed iterative algorithms can successfully achieve consensus in complex multiagent systems.
    • The framework effectively handles noisy channels and diverse agent interactions (cooperation/competition).
    • Simulation results validate the theoretical findings and algorithm performance.