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Related Concept Videos

Protein Networks02:26

Protein Networks

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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,...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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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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Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

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Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
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Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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Updated: Oct 15, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Identifying critical higher-order interactions in complex networks.

Mehmet Emin Aktas1, Thu Nguyen2, Sidra Jawaid3

  • 1Department of Mathematics and Statistics, University of Central Oklahoma, Edmond, OK, 73034, USA. maktas@uco.edu.

Scientific Reports
|October 29, 2021
PubMed
Summary
This summary is machine-generated.

Higher-order interactions are critical in complex networks, surpassing the influence of pairwise edges in diffusion processes. Novel methods effectively identify these crucial higher-order interactions for better network understanding.

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Area of Science:

  • Network science
  • Complex systems analysis
  • Mathematical modeling

Background:

  • Diffusion processes are fundamental in network science, influencing information spread, disease contagion, and system failures.
  • Network interactions can be pairwise (edges) or involve multiple nodes (higher-order), with higher-order interactions significantly impacting network dynamics.
  • Identifying critical interactions is key to understanding and controlling network behavior.

Purpose of the Study:

  • To introduce a novel methodology for identifying critical higher-order interactions in complex networks.
  • To generalize existing graph centrality measures to account for higher-order interactions.
  • To evaluate the significance of higher-order interactions compared to traditional edge-based interactions.

Main Methods:

  • Development of two new generalized Laplacians to extend centrality measures to higher-order networks.
  • Comparison of generalized centrality measures using the size of the giant component.
  • Utilizing the Susceptible-Infected-Recovered (SIR) simulation model to assess diffusion dynamics.

Main Results:

  • Higher-order interactions were found to play a more critical role in diffusion than pairwise edges.
  • The proposed generalized centrality measures effectively identified critical higher-order interactions.
  • Both giant component analysis and SIR simulations confirmed the superior impact of higher-order interactions.

Conclusions:

  • Higher-order interactions are more influential than edges in network diffusion processes.
  • The novel Laplacians and generalized centrality measures provide a promising approach for identifying critical higher-order interactions.
  • Understanding higher-order interactions is essential for effective network analysis and control.