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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

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Related Experiment Video

Updated: May 22, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Targeting the dynamics of complex networks.

Ricardo Gutiérrez, Irene Sendiña-Nadal, Massimiliano Zanin

    Scientific Reports
    |May 8, 2012
    PubMed
    Summary
    This summary is machine-generated.

    We developed a method to control network dynamics using a Master Stability Function. Node degree is key for targeting, proving most effective in scale-free networks for broad applicability.

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    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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    JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    Area of Science:

    • Network science
    • Control theory
    • Dynamical systems

    Background:

    • Controlling complex network dynamics is crucial for understanding and managing systems.
    • Existing methods may lack generality or efficiency in targeting specific network evolutions.

    Purpose of the Study:

    • To present a generic procedure for steering network dynamics towards a desired evolution.
    • To identify key factors influencing the effectiveness of network targeting strategies.

    Main Methods:

    • Utilizing a Master Stability Function (MSF) approach to assess the stability of target dynamics.
    • Implementing a node selection strategy based on network properties, specifically node degree.

    Main Results:

    • Demonstrated that node degree is a critical parameter in selecting nodes for effective targeting.
    • Showed that the proposed targeting mechanism is highly effective in heterogeneous scale-free network architectures.

    Conclusions:

    • The developed generic procedure offers a robust method for controlling network dynamics.
    • The findings highlight the importance of network topology, particularly node degree in scale-free networks, for successful dynamic steering.