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MoCha: Molecular Characterization of Unknown Pathways.

Daniel Lobo1, Jennifer Hammelman2, Michael Levin2

  • 11 Department of Biological Sciences, University of Maryland , Baltimore County, Baltimore, Maryland.

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|March 8, 2016
PubMed
Summary

Automated network inference methods can identify unknown proteins and pathways. MoCha (Molecular Characterization) is a new tool that efficiently searches protein-protein interactions to identify these unknowns, aiding model validation.

Keywords:
data miningpathwaysprotein–protein interactionregulatory networks

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Area of Science:

  • Systems Biology
  • Bioinformatics

Background:

  • Automated methods for reverse-engineering complex regulatory networks enable mechanistic model inference from experimental data.
  • These methods can identify unknown components and pathways crucial for model validation.
  • Currently, no efficient tools exist to identify the molecular nature of these unknown components.

Purpose of the Study:

  • To present MoCha (Molecular Characterization), a tool for identifying unknown proteins and pathways.
  • To aid in the characterization of unknown components within reverse-engineered biological networks.

Main Methods:

  • MoCha utilizes the STRING database, containing over a billion protein-protein interactions across 2,000 organisms.
  • The tool is optimized for rapid searching of protein-protein interaction data.
  • Searches are typically completed within seconds.

Main Results:

  • MoCha was demonstrated to successfully characterize unknown components from literature-based reverse-engineered models.
  • The tool efficiently identifies potential proteins and pathways associated with unknown network elements.

Conclusions:

  • MoCha is a valuable and efficient tool for characterizing unknown pathways and proteins.
  • It serves as a useful aid for manual network model analysis or as a downstream step in automated model inference workflows.