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Related Concept Videos

Protein Networks02:26

Protein Networks

4.0K
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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Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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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...
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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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Fischer Projections02:18

Fischer Projections

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Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
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Updated: Aug 19, 2025

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

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Hypergraph geometry reflects higher-order dynamics in protein interaction networks.

Kevin A Murgas1, Emil Saucan2, Romeil Sandhu3,4

  • 1Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA. kevin.murgas@stonybrookmedicine.edu.

Scientific Reports
|December 3, 2022
PubMed
Summary
This summary is machine-generated.

We introduce a hypergraph model to analyze complex protein interactions, revealing increased network curvature in stem and cancer cells. This approach enhances understanding of cellular dynamics and disease pathways.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Science

Background:

  • Protein interactions are crucial for cellular phenotype and function.
  • Standard graph models for protein networks are limited to pairwise relationships.
  • Higher-order interactions, like protein complexes and regulatory loops, are biologically significant.

Purpose of the Study:

  • To present a novel hypergraph model for analyzing dynamic gene expression data.
  • To quantify network heterogeneity using Forman-Ricci curvature.
  • To compare the hypergraph approach with traditional graph-based and differential gene expression methods.

Main Methods:

  • Development of a hypergraph model to represent higher-order biological relationships.
  • Application of Forman-Ricci curvature to quantify network heterogeneity.
  • Analysis of dynamic gene expression datasets, including pluripotent stem cells, cancer cells, and a melanoma dataset.

Main Results:

  • Global network analysis revealed increased curvature in pluripotent stem cells and cancer cells.
  • Local curvature analysis identified altered oncogenic and tumor suppressor pathways in melanoma.
  • The hypergraph model demonstrated advantages over graph-based and differential gene expression approaches.

Conclusions:

  • Hypergraph models offer a more comprehensive representation of biological networks than traditional graphs.
  • Network curvature is a sensitive metric for characterizing cellular states and identifying disease-related pathways.
  • This approach provides novel insights into the dynamics of cellular processes and disease mechanisms.