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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,...
IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and produces two-second...
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...
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Shortest path discovery of complex networks.

Attila Fekete1, Gábor Vattay, Márton Pósfai

  • 1Department of Physics of Complex Systems, Eötvös University, Pázmány P. sétány 1/A., H-1117 Budapest, Hungary. fekete@complex.elte.hu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 8, 2009
PubMed
Summary

This study analyzes network sampling, revealing that discovered edges in finite networks grow slower than previously thought. Sampled networks exhibit properties similar to networks with randomly removed edges.

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Area of Science:

  • Network science
  • Graph theory
  • Statistical physics

Background:

  • Understanding network structure is crucial for various fields.
  • Shortest-path sampling is a key method for network analysis.
  • Existing mean-field models may overestimate edge discovery in finite networks.

Purpose of the Study:

  • To analytically study sampled networks using shortest-path sampling models.
  • To derive formulas for edge discovery probability in static and evolving networks.
  • To characterize the properties of sampled networks, including their degree distribution.

Main Methods:

  • Analytic study of sampled networks.
  • Development of formulas for edge discovery probability.
  • Calculation of degree distribution for sampled networks.

Main Results:

  • Analytic formulas for edge discovery probability in static and evolving network models.
  • Demonstration that discovered edges in finite networks scale slower than predicted by mean-field models.
  • Sampled networks show degree distributions analogous to networks with random edge removal.

Conclusions:

  • The study provides a more accurate analytic understanding of shortest-path sampling in networks.
  • Findings challenge previous predictions regarding edge discovery scaling.
  • The analogy to randomly destroyed networks offers new insights into sampled network structures.