Evidence of Probabilistic Behaviour in Protein Interaction Networks

Abstract

Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

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Document Details

Document Type
Technical Report
Publication Date
Jan 31, 2008
Accession Number
ADA480739

Entities

People

  • Anders Wallqvist
  • Jaques Reifman
  • Joseph Ivanic

Organizations

  • United States Army Medical Research and Development Command

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Application Software
  • Biological Phenomena
  • Biological Processes
  • Biology
  • Biomedical Research
  • Cells
  • Computational Biology
  • Drosophila
  • Escherichia Coli
  • Malaria
  • Observation
  • Probability
  • Protein-Protein Interactions
  • Proteins
  • Systems Biology
  • Throughput
  • United States

Fields of Study

  • Biology

Readers

  • Educational Psychology
  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference