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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-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...

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Related Experiment Video

Updated: Jun 18, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Algorithms for effective querying of compound graph-based pathway databases.

Ugur Dogrusoz1, Ahmet Cetintas, Emek Demir

  • 1Computer Engineering Dept, Bilkent University, Center for Bioinformatics, Ankara, Turkey. ugur@cs.bilkent.edu.tr

BMC Bioinformatics
|November 18, 2009
PubMed
Summary
This summary is machine-generated.

We developed a novel querying framework with graph-theoretic algorithms for analyzing complex biological pathway databases. This tool efficiently extracts subnetworks from hierarchical graphs, aiding in understanding cellular processes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Graph-based pathway ontologies are crucial for integrating and analyzing cellular networks using graph theory.
  • Hierarchically structured (compound) graphs offer benefits like complexity reduction by decomposing networks into modules.
  • Efficient querying of large, integrated compound networks is essential for extracting specific subnetworks.

Purpose of the Study:

  • To develop a querying framework and algorithms for analyzing complex, hierarchical biological pathway databases.
  • To enable efficient extraction of subnetworks from integrated pathway data.
  • To support the investigation of structural and dynamic properties of cellular networks.

Main Methods:

  • Development of a querying framework incorporating graph-theoretic algorithms (neighborhood queries, shortest paths, feedback loops).
  • Implementation of algorithms capable of handling compound/nested graph structures and ubiquitous entities.
  • Design of a recursive query organization using "AND" and "OR" operators for complex query construction.

Main Results:

  • A unique querying framework applicable to diverse pathway databases (PPIs, metabolic, signaling).
  • The framework effectively handles nested structures and complex relationships within pathway data.
  • Algorithms implemented in PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) version 2.1.

Conclusions:

  • The developed framework and algorithms are effective for querying large, compound biological pathway databases.
  • The PATIKAweb software tool provides efficient solutions for biologically significant questions.
  • The PATIKA Project website and PATIKAweb are available for public access.