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Related Concept Videos

Introduction to Metabolism01:30

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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Optimized Automated Analysis of Live Neuronal Mitochondria Homeostasis Modulation by Isoform-Specific Retinoic Acid Receptors
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Optimization based automated curation of metabolic reconstructions.

Vinay Satish Kumar1, Madhukar S Dasika, Costas D Maranas

  • 1Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA. vsk111@psu.edu <vsk111@psu.edu>

BMC Bioinformatics
|June 23, 2007
PubMed
Summary

This study presents a novel optimization method to identify and fill gaps in metabolic reconstructions. The approach systematically bridges missing functionalities, improving the accuracy of genome-scale models for organisms like E. coli and S. cerevisiae.

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Area of Science:

  • Metabolic Engineering
  • Computational Biology
  • Systems Biology

Background:

  • Genome-scale metabolic reconstructions are essential tools but often incomplete due to missing experimental or homology data.
  • Identifying and bridging these gaps is a critical challenge in automated reconstruction generation.

Purpose of the Study:

  • To develop and demonstrate an optimization-based procedure for identifying and eliminating network gaps in metabolic reconstructions.
  • To propose hypotheses for experimentally validating the identified gaps and their proposed solutions.

Main Methods:

  • An optimization-based procedure was developed to identify metabolites with no production under any uptake conditions.
  • Connectivity was restored using four mechanisms: reaction directionality reversal, addition of reactions from other organisms, external transport mechanisms, and intracellular transport reactions.
  • The method was applied to genome-scale reconstructions of Escherichia coli and Saccharomyces cerevisiae.

Main Results:

  • Approximately 10% of metabolites in E. coli and 30% in S. cerevisiae were found to be unable to carry flux.
  • The dominant mechanism for restoring metabolic flow was the reversal of existing reaction directionalities.
  • Compartmentalization in S. cerevisiae presented a more complex network topology.

Conclusions:

  • Systematic methods for identifying and filling gaps in genome-scale metabolic reconstructions have been proposed.
  • Gap filling can involve modifications to existing models or the addition of reactions reconciled from multi-organism databases.
  • The computational results offer testable hypotheses for experimental validation.