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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-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...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...

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Related Experiment Video

Updated: May 31, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Identification of directed interactions in networks.

K A Lindsay1, J R Rosenberg

  • 1School of Mathematics & Statistics, University of Glasgow, University Gardens, Glasgow, G12 8QW, UK. kal@maths.gla.ac.uk

Biological Cybernetics
|June 17, 2011
PubMed
Summary
This summary is machine-generated.

This study reveals limitations in current autoregressive models for neuroscience data analysis. A novel coherence-based method is introduced to better understand directional interactions and unobserved processes in neural networks.

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Area of Science:

  • Neuroscience
  • Stochastic Processes
  • Network Analysis

Background:

  • Multichannel data collection is standard in neuroscience, requiring methods to determine interaction directionality.
  • Autoregressive models are commonly used but possess inherent limitations for directional analysis.
  • Previous research has indicated potential difficulties with autoregressive approaches in complex systems.

Purpose of the Study:

  • To demonstrate the intrinsic difficulties within autoregressive models for directional interaction detection.
  • To introduce a new method for analyzing directional interactions in neuroscientific data, accounting for unobserved processes.
  • To explore the utility of coherence measures in network configuration analysis.

Main Methods:

  • Demonstration of inherent limitations in autoregressive models.
  • Introduction of a novel method based on coherence, incorporating unobserved processes.
  • Analysis of two three-process network examples to validate the coherence-based approach.

Main Results:

  • Coherence measures, while non-directional, are uniquely associated with specific network configurations.
  • The coherence-based method can specify all network configurations consistent with observed coherences.
  • The method elucidates relationships among unobserved processes within neural networks.

Conclusions:

  • The proposed coherence-based method offers an advancement over autoregressive models for analyzing directional interactions in neuroscience.
  • This approach effectively addresses unobserved processes and provides insights into network dynamics.
  • The method allows for progressive resolution of unobserved process relationships as new data becomes available.