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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Related Experiment Video

Updated: Apr 13, 2026

Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry
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PCD-GED: Protein complex detection considering PPI dynamics based on time series gene expression data.

Amir Lakizadeh1, Saeed Jalili1, Sayed-Amir Marashi2

  • 1Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.

Journal of Theoretical Biology
|May 3, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for detecting dynamic protein complexes using time-series gene expression and protein-protein interaction (PPI) networks. The approach enhances understanding of cellular machinery by analyzing temporal network dynamics.

Keywords:
Clustering coefficientProtein complexProtein–protein interactionWeighted density

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Protein complexes are crucial for cellular functions, but static protein-protein interaction (PPI) networks fail to capture their dynamic nature.
  • Understanding dynamic protein complexes requires integrating temporal data with interaction networks.

Purpose of the Study:

  • To develop a novel computational method for detecting dynamic protein complexes.
  • To improve the accuracy of protein complex detection by incorporating time-series gene expression data.

Main Methods:

  • The proposed method constructs time-sequenced subnetworks (TSNs) based on interaction activation times.
  • Protein complexes are identified within each TSN using weighted clustering coefficient and maximal weighted density algorithms.
  • The final set of complexes is derived from the union of complexes identified across all TSNs.

Main Results:

  • The integration of time-series gene expression data significantly improves protein complex detection.
  • The method effectively captures the dynamic nature of protein complexes within cellular networks.
  • The proposed approach demonstrates superior performance compared to methods relying solely on static PPI networks.

Conclusions:

  • Dynamic analysis of protein-protein interactions using time-series data is essential for accurate protein complex detection.
  • The developed method offers a robust framework for uncovering functional protein modules in biological systems.
  • This approach provides valuable insights into the dynamic organization and function of the cell machinery.