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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...
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...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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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

Maximally informative pairwise interactions in networks.

Jeffrey D Fitzgerald1, Tatyana O Sharpee

  • 1Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a method to optimize biological network mappings for maximal information transfer using the Ising model. It reveals how network interactions vary with input signal types and noise levels.

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Area of Science:

  • Computational Biology
  • Statistical Physics
  • Network Science

Background:

  • Biological networks often exhibit complex interactions.
  • Maximum entropy models, specifically the Ising model, effectively describe these networks using pairwise interactions.
  • Understanding optimal network states for information processing is crucial.

Purpose of the Study:

  • To develop an approach for optimizing input-signal-to-network-state mappings.
  • To maximize information transfer from a given input distribution within biological networks.
  • To derive methods for calculating Ising model parameters and assessing model applicability.

Main Methods:

  • Formulating an optimization problem for network mappings.
  • Deriving linear equations for Ising model coupling constants.
  • Analyzing geometric properties related to pairwise Ising model applicability.

Main Results:

  • Optimal pairwise interactions are zero for simple inputs (Gaussian, uniform) but non-zero for naturalistic inputs.
  • Non-zero interactions strengthen with increased noise in network node response functions.
  • The approach provides a framework for inferring interactions with unmeasured network components.

Conclusions:

  • The developed method optimizes information flow in biological networks.
  • Network interaction properties are dependent on input signal characteristics and noise.
  • This framework aids in understanding and inferring complex biological network dynamics.