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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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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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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological

Yusuke Tokuhara1, Tatsuya Akutsu2, Jean-Marc Schwartz3

  • 1Department of Information Science, Faculty of Science, Toho University, Funabashi, Chiba, Japan.

NPJ Systems Biology and Applications
|August 12, 2024
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Summary
This summary is machine-generated.

This study introduces a new algorithm to identify critical control nodes in complex biological networks with uncertain interactions. The method efficiently finds key molecules driving network function, aiding disease research.

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Area of Science:

  • Systems Biology
  • Network Science
  • Computational Biology

Background:

  • Network controllability integrates control theory with network structure for biological systems.
  • Identifying critical nodes is crucial for understanding network control, but existing methods struggle with probabilistic interactions.

Purpose of the Study:

  • To develop an efficient algorithm for determining critical control nodes in probabilistic directed networks.
  • To integrate the probabilistic nature of molecular interactions into network control models.

Main Methods:

  • A probabilistic control model based on the minimum dominating set framework was developed.
  • Mathematical tools were created to enhance the efficiency of determining critical control nodes in large networks.

Main Results:

  • The algorithm efficiently identifies critical control nodes in probabilistic directed networks.
  • Application to the human intracellular signal transduction network linked critical nodes to disease-associated genes, including SARS-CoV-2 targets.

Conclusions:

  • The proposed methodology provides a practical and efficient way to analyze critical control nodes in probabilistic biological networks.
  • This approach can advance the study of diverse biological systems with uncertain or probabilistic interactions.