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Introduction to Metabolism

Metabolism encompasses all biochemical reactions in a living organism, facilitating both the breakdown and synthesis of biomolecules. These metabolic processes are categorized into catabolic and anabolic pathways, which operate in a coordinated manner to ensure energy balance and cellular function.Catabolic Pathways and Energy ReleaseCatabolic pathways involve the breakdown of complex macromolecules such as carbohydrates, lipids, and proteins into smaller structures like monosaccharides, fatty...
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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Published on: February 8, 2017

A fast and accurate algorithm for comparative analysis of metabolic pathways.

Ferhat Ay1, Tamer Kahveci, Valérie DE Crécy-Lagard

  • 1Department of Computer Science and Engineering, University of Florida, Gainesville, FL 32611, USA. fay@cise.ufl.edu

Journal of Bioinformatics and Computational Biology
|June 10, 2009
PubMed
Summary

This study introduces a novel algorithm for aligning metabolic pathways, considering diverse biochemical entities like enzymes, reactions, and compounds. The method efficiently identifies functional similarities without information loss, enabling rapid biological discovery.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Published on: July 1, 2020

Area of Science:

  • Biochemistry and Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Comparative pathway analysis is vital for understanding organism survival and identifying functional similarities.
  • Aligning metabolic pathways is challenging due to diverse entity types (enzymes, reactions, compounds).
  • Existing methods often oversimplify pathways, losing crucial information content.

Purpose of the Study:

  • To develop an algorithm for pairwise alignment of metabolic pathways.
  • To address the challenge of aligning pathways with heterogeneous entities.
  • To preserve the full information content of metabolic pathways during alignment.

Main Methods:

  • Developed a novel algorithm for pairwise metabolic pathway alignment.
  • Incorporated both entity homology and interaction topology into the alignment process.
  • Utilized eigenvalue problems for each entity type and enforced consistency using reachability sets.

Main Results:

  • The algorithm aligns diverse entities such as enzymes, reactions, and compounds.
  • It avoids information loss by not abstracting pathway models.
  • Achieved biologically and statistically significant alignments in milliseconds.

Conclusions:

  • The new algorithm effectively aligns metabolic pathways without information loss.
  • It considers both entity similarity and interaction organization.
  • Offers a computationally efficient solution for complex pathway alignment problems.