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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...
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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...
Cell-matrix's Response to Mechanical Forces01:13

Cell-matrix's Response to Mechanical Forces

In animal cells, the extracellular matrix allows cells within tissues to withstand external stresses and transmits signals from the outside of the cell to the inside. The extracellular matrix is extensive, and its composition varies between different types of tissues. For example, the reticular fibers and ground substance make up the ECM in loose connective tissue, while collagen and bone minerals make up the ECM of bone tissue. 
Anchoring junctions mechanically attach a cell to the...

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Updated: Jun 9, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Towards a matrix mechanics framework for dynamic protein network.

Sanjoy K Bhattacharya1

  • 1Bascom Palmer Eye Institute, University of Miami, 1638 NW 10th Avenue, Suite 706A, Miami, FL 33136 USA.

Systems and Synthetic Biology
|September 1, 2010
PubMed
Summary
This summary is machine-generated.

This study proposes a new mathematical framework for dynamic protein interactions within cells. This approach will enhance understanding of cellular processes and improve biopharmaceutical production.

Keywords:
ChannelingDynamic networkMathematical foundationMatrix mechanicsProtein–protein interactionTime-resolved proteomics

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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Related Experiment Videos

Last Updated: Jun 9, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Area of Science:

  • Cellular Biology
  • Biophysics
  • Computational Biology

Background:

  • Current protein-protein interaction networks are static and lack temporal resolution.
  • Existing computational tools cannot model dynamic, compartmentalized intracellular interactions.
  • A mathematical foundation for interactions in viscous cellular environments is missing.

Purpose of the Study:

  • To develop a mathematical foundation and computational algorithm for dynamic, compartmentalized protein interaction networks.
  • To enable time-resolved modeling of intracellular protein interactions.
  • To bridge the gap between static network visualization and dynamic cellular processes.

Main Methods:

  • Utilizing high-throughput, time-resolved proteomics to capture dynamic molecular changes in selected proteins.
  • Developing a computational algorithm based on acquired dynamic interaction data.
  • Building a mathematical framework for particle interactions within a viscous liquid state.

Main Results:

  • Establishment of a novel mathematical foundation for dynamic intracellular protein interactions.
  • Development of a computational algorithm for building compartmentalized, time-resolved protein networks.
  • Demonstration of potential applications in understanding genetic disorders and biopharmaceutical production.

Conclusions:

  • The proposed framework enables dynamic modeling of protein interactions, overcoming limitations of static networks.
  • This advancement is crucial for understanding complex physiological phenomena like incomplete penetrance.
  • The methodology promises significant improvements in biopharmaceutical manufacturing efficiency.