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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...
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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An in silico method for detecting overlapping functional modules from composite biological networks.

Ioannis A Maraziotis1, Konstantina Dimitrakopoulou, Anastasios Bezerianos

  • 1Department of Medical Physics, School of Medicine, University of Patras, GR26500 Patras, Greece. ioannis@heart.med.upatras.gr

BMC Systems Biology
|November 4, 2008
PubMed
Summary

We developed DetMod, a novel algorithm that integrates gene expression and protein-protein interaction data to identify functional modules in yeast. This method successfully captures module crosstalk and biological relationships, outperforming existing approaches.

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Area of Science:

  • Systems Biology
  • Network Biology
  • Computational Biology

Background:

  • Increasing volumes of gene expression and protein-protein interaction (PPI) data aid cellular dynamics understanding.
  • Functional module detection is key to deciphering biological network modularity.
  • Existing network partitioning algorithms often overlook functional relationships, focusing solely on topology.

Purpose of the Study:

  • To integrate heterogeneous proteomics and microarray data from yeast into a weighted PPI graph.
  • To develop and apply a novel algorithm, DetMod, for partitioning this enriched PPI network.
  • To identify and characterize functional modules within the yeast interactome.

Main Methods:

  • Integration of yeast proteomics and microarray data to construct a weighted PPI graph.
  • Application of the novel DetMod algorithm for network partitioning.
  • Analysis of module properties, including inter-module cross-talk, GO term similarity, and incorporation of known protein complexes.

Main Results:

  • Identification of 335 functional modules in the yeast interactome.
  • DetMod effectively captures inter-module cross-talk through controlled module overlap.
  • Modules exhibit dense protein interactions and high similarity in biological process Gene Ontology (GO) terms.
  • Known protein complexes are substantially represented within the identified modules.
  • Demonstration of DetMod's superior performance compared to other computational approaches.

Conclusions:

  • The integration of heterogeneous data and the DetMod algorithm yield confident functional modules.
  • The proposed approach proves superior to methods relying solely on PPI data.
  • DetMod offers a robust framework for functional module discovery in biological networks.