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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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Protein-protein Interfaces02:04

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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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Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast

Nitin Singh1, Mehmet Eren Ahsen2, Niharika Challapalli3

  • 1Department the Department of Bioengineering at the University of Texas at Dallas.

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
|December 14, 2020
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This study introduces a novel algorithm for constructing gene interaction networks (GINs) with directed, weighted edges and cycles, improving biological realism. The new method identifies essential genes for cancer cell survival and reveals subtype-specific network properties.

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Area of Science:

  • Systems biology
  • Computational biology
  • Bioinformatics

Background:

  • Gene interaction networks (GINs) are crucial for understanding biological systems.
  • Existing algorithms for GIN construction often yield biologically unrealistic networks (undirected, unweighted, or acyclic).
  • High-throughput gene expression data offers a rich source for inferring complex biological networks.

Purpose of the Study:

  • To develop a novel algorithm for constructing more biologically realistic GINs.
  • To infer and analyze GINs for lung and breast cancer subtypes.
  • To validate the biological relevance of inferred network properties.

Main Methods:

  • Developed a new algorithm using the phi-mixing coefficient from probability theory.
  • Inferred weighted, directed GINs with cycles for small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), and normal lung tissue.
  • Analyzed gene essentiality using siRNA screening data and transcription factor target enrichment via ChIP-Seq.

Main Results:

  • The new algorithm successfully generated directed, weighted GINs that permit cycles.
  • Gene degree in the inferred NSCLC network correlated with gene essentiality for cell survival.
  • The SCLC network, unlike the NSCLC network, showed enrichment for ASCL1 ChIP-Seq neighbors, indicating subtype-specific regulatory mechanisms.
  • Whole-genome interaction networks were reverse-engineered for Luminal-A and Basal breast cancer subtypes.

Conclusions:

  • The proposed algorithm provides a more realistic approach to constructing GINs from gene expression data.
  • Network topology, specifically gene degree, can predict gene essentiality in cancer.
  • GINs can reveal subtype-specific molecular mechanisms, such as the role of ASCL1 in SCLC.
  • This method offers a powerful tool for comparative network analysis across different cancer types and subtypes.