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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,...
Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Computational approaches for analyzing information flow in biological networks.

Boris Kholodenko1, Michael B Yaffe, Walter Kolch

  • 1Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.

Science Signaling
|April 19, 2012
PubMed
Summary

Advancements in omics technologies generate vast biological data. Network analysis and computational modeling are crucial for understanding how these molecular players interact in cellular signaling, with applications in medicine.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Omics technologies (genomics, proteomics, metabolomics, lipidomics) have produced large datasets of biological molecules.
  • Understanding how these molecules interact within cellular signaling networks remains a significant challenge.

Purpose of the Study:

  • To review experimental and theoretical progress in mathematical and computational modeling of biological signaling networks.
  • To highlight future challenges in network analysis for understanding cellular responses and medical applications.

Main Methods:

  • Focus on network reconstruction from experimental data.
  • Analysis of network structures that control biological information flow.
  • Examination of network design principles in specifying cellular decisions.

Main Results:

  • Mathematical and computational modeling are essential tools for deciphering complex biological systems.
  • Network analysis provides critical insights into cellular signaling pathways.
  • Understanding network architecture is key to predicting cellular behavior.

Conclusions:

  • Network analysis of omics data is vital for understanding cellular regulation.
  • This approach offers significant potential for advancing medical research and applications.
  • Future work should focus on integrating diverse omics data and refining network models.